├── IJCAI-2018-DOG.pptx ├── README.md └── eda_solve.ipynb /IJCAI-2018-DOG.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/plantsgo/ijcai-2018/19d144572cc51eeccdbe7047c5c31f496562ca80/IJCAI-2018-DOG.pptx -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ijcai-2018 2 | ijcai-2018 top1 solution 3 | 一.Graft Learning 4 | 首先科普一下什么是嫁接。 5 | 6 | 在生产实践过程中，嫁接对经济价值的提高，也有着非常多的实例：如普通的水杉，价值一元；而通过嫁接手段，培育成金叶水杉后，经济价值提高20多倍；再如普通的大叶女贞树，价值几角钱；而通过嫁接的手段，培育成彩叶桢树后，其经济价值更高达近百倍。由此可见，嫁接对品种的改良，经济价值的提高都有首非常重要的意义。 7 | 8 | 影响嫁接成活的主要因素是接穗和砧木的亲和力，其次是嫁接的技术和嫁接后的管理。所谓亲合力，就是接穗和砧木在内部组织结构上、生理和遗传上，彼此相同或相近，从而能互相结合在一起的能力。亲和力高，嫁接成活率高。反之，则成活率低。一般来说，植物亲缘关系越近，则亲和力越强。例如苹果接于沙果；梨接于杜梨、秋子梨；柿接于黑枣；核桃接于核桃楸等亲和力都很好。 9 | 10 | 大家应该都尝试过只用只用前七天的数据来预测，最后一天来做线下验证，这种效果不太好，但是数据量大，就如同这里的砧木获取容易，但是实际价值不大。 11 | 大家应该也尝试过只用最后半天来做预测，达到的效果也已经很不错了，并且比只用前七天的训练好，但是数据量比较少。 12 | 13 | 那么我们参考嫁接的思想，让砧木来给我们的接穗输送营养。在这里前七天的数据就类似于砧木，后半天的数据类似于接穗。前七天预测出来的结果就是营养。这样我们预测出来的结果也更加接近于最后一天。 14 | 15 | 那么既然这样我为什么没有使用初赛的数据来做“嫁接“呢？ 16 | 亲缘关系越近的嫁接成功率越高，在这个问题上也是item相同品别的做这种“嫁接“操作效果才会好，而我们的初赛数据和复赛数据的item并不是一个品类的，所以价值不大。 17 | 18 | 二.Sample Embedding 19 | sample_emb_x=[x1,x2,x3,x4,...,xn] xn为第n个property在不在predict_category_property中 20 | sample_emb_y=[y1,y2,y3,y4,...,yn] yn为第n个property在不在item_property_list中 21 | 22 | 一个user有很多个不同的item交互样本，一个item也有很多不同的user交互样本 23 | user_emb_x=mean([sample_emb_x_1,sample_emb_x_2,...,sample_emb_x_k]) sample_emb_x_k为该user的第k条样本的sample_emb 24 | user_emb_y=mean([sample_emb_y_1,sample_emb_y_2,...,sample_emb_y_k]) sample_emb_y_k为该user的第k条样本的sample_emb 25 | 26 | 通过这种对所有样本的sample_emb做mean操作来对user做embedding 27 | item_emb_x=mean([user_emb_x_1,user_emb_x_2,...,user_emb_x_k]) user_emb_x_k为该item的第k条样本的user_emb 28 | item_emb_y=mean([user_emb_y_1,user_emb_y_2,...,user_emb_y_k]) user_emb_y_k为该item的第k条样本的user_emb 29 | 30 | 通过这种对所有样本的use_emb做mean操作来对item做embedding 31 | 至此通过predict_category_property，item_property_list这两条信息对sample，user，item做了embedding。 32 | 得到了6*n个特征，n的大小视情况而定，这里我取了出现次数top100的property来做我的embedding，所以总共6*100个特征。 33 | -------------------------------------------------------------------------------- /eda_solve.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np \n", 12 | "import pandas as pd \n", 13 | "import matplotlib.pyplot as plt\n", 14 | "import matplotlib as mpl\n", 15 | "import seaborn as sns\n", 16 | "from scipy import interpolate\n", 17 | "color = sns.color_palette()\n", 18 | "%matplotlib inline\n", 19 | "import sys \n", 20 | "reload(sys)\n", 21 | "sys.setdefaultencoding('gbk') \n", 22 | "mpl.rcParams['font.sans-serif'] = ['SimHei']" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 17, 28 | "metadata": { 29 | "collapsed": true 30 | }, 31 | "outputs": [], 32 | "source": [ 33 | "train=pd.read_csv(\"round2_train.txt\",sep=\" \")\n", 34 | "test_a=pd.read_csv(\"round2_ijcai_18_test_a_20180425.txt\",sep=\" \")\n", 35 | "test_b=pd.read_csv(\"round2_ijcai_18_test_b_20180510.txt\",sep=\" \")" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 18, 41 | "metadata": {}, 42 | "outputs": [ 43 | { 44 | "data": { 45 | "image/png": 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OeSjwycxc39xKJozrgIeWTBgBMvP8HccLXA38z0KhTwY+1nxI7xsRTy8U9z3A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\nNEqSJKmVSaMkSZJa/X+393uiIFSezwAAAABJRU5ErkJggg==\n", 46 | "text/plain": [ 47 | "" 48 | ] 49 | }, 50 | "metadata": {}, 51 | "output_type": "display_data" 52 | } 53 | ], 54 | "source": [ 55 | "import time\n", 56 | "train[\"time_sting\"]=train[\"context_timestamp\"].apply(lambda x:time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(x)))\n", 57 | "train[\"time_sting\"]=pd.to_datetime(train[\"time_sting\"])\n", 58 | "train[\"hour\"]=train[\"time_sting\"].dt.hour\n", 59 | "train[\"day\"]=train[\"time_sting\"].dt.day\n", 60 | "train[\"day\"]=train[\"day\"].apply(lambda x:0 if x==31 else x)\n", 61 | "\n", 62 | "grouped_df = train.groupby([\"day\", \"hour\"])[\"is_trade\"].aggregate(\"mean\").reset_index()\n", 63 | "grouped_df = grouped_df.pivot('day', 'hour', 'is_trade')\n", 64 | "\n", 65 | "plt.figure(figsize=(12,6))\n", 66 | "sns.heatmap(grouped_df)\n", 67 | "plt.title(\"CVR of Day Vs Hour\")\n", 68 | "plt.show()\n" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 19, 74 | "metadata": {}, 75 | "outputs": [ 76 | { 77 | "data": { 78 | "image/png": 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trNnO8wAAYLdY+vSPQ0nu2Dy+O8mV21yznecBAMCucM6xY8cW+5dvnsLx0939QFV9d5KX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\n", 79 | "text/plain": [ 80 | "" 81 | ] 82 | }, 83 | "metadata": {}, 84 | "output_type": "display_data" 85 | } 86 | ], 87 | "source": [ 88 | "cnt_srs=train.groupby(\"day\").is_trade.mean()\n", 89 | "\n", 90 | "plt.figure(figsize=(12,6))\n", 91 | "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8, color=color[0])\n", 92 | "plt.ylabel('CVR', fontsize=12)\n", 93 | "plt.xlabel('day', fontsize=12)\n", 94 | "plt.show()" 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "metadata": {}, 100 | "source": [ 101 | "通过对每天每时转化率分析，前几天转化率和当天差异较大，所以直接使用最后一天的上午作为训练集。" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 21, 107 | "metadata": {}, 108 | "outputs": [ 109 | { 110 | "data": { 111 | "image/png": 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sNnymvLv3d/fe7t6b5KkkP5TkwXdqUyC/Icn27r4iya6q2j1PbaOvEQAA1mLDZ8rfUVVf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\n", 112 | "text/plain": [ 113 | "" 114 | ] 115 | }, 116 | "metadata": {}, 117 | "output_type": "display_data" 118 | } 119 | ], 120 | "source": [ 121 | "cnt_srs=train.groupby(\"day\").is_trade.count()\n", 122 | "\n", 123 | "plt.figure(figsize=(12,6))\n", 124 | "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8, color=color[0])\n", 125 | "plt.ylabel('count', fontsize=12)\n", 126 | "plt.xlabel('day', fontsize=12)\n", 127 | "plt.show()" 128 | ] 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "metadata": { 133 | "collapsed": true 134 | }, 135 | "source": [ 136 | "但是前七天包含了大量的数据，使用前七天提取了转化率信息，同时使用前七天的数据预测了第八天的数据，然后把预测的结果当成新的特征。" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": 22, 142 | "metadata": {}, 143 | "outputs": [ 144 | { 145 | "data": { 146 | "image/png": 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kjyTTT7xgod8P2n/nYcqJJEmSlmaupEuSJEkdY5AuSZIkdYxBuiRJktQxBumSJElSxxik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\n00uIlZ61WmU6mpg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147 | "text/plain": [ 148 | "" 149 | ] 150 | }, 151 | "metadata": {}, 152 | "output_type": "display_data" 153 | } 154 | ], 155 | "source": [ 156 | "cnt_srs=pd.DataFrame(train.groupby(\"user_id\").is_trade.count()).reset_index()\n", 157 | "cnt_srs.columns=[\"user_id\",\"user_id_count\"]\n", 158 | "\n", 159 | "cnt_srs=cnt_srs.groupby(\"user_id_count\").user_id.count()\n", 160 | "\n", 161 | "plt.figure(figsize=(12,6))\n", 162 | "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8, color=color[0])\n", 163 | "plt.ylabel('user_count', fontsize=12)\n", 164 | "plt.xlabel('user_id_count', fontsize=12)\n", 165 | "plt.show()" 166 | ] 167 | }, 168 | { 169 | "cell_type": "markdown", 170 | "metadata": { 171 | "collapsed": true 172 | }, 173 | "source": [ 174 | "发现每个人的平均次数较少，所以和通常的根据用户的历史数据预测未来是否购买问题是不一样的，没必要进行滑窗统计user_id的特征。\n", 175 | "进一步分析，数据中包含大量的单次用户或很少次数的用户，猜测这里只是用户的搜索数据，浏览数据和点击数据不包含在里面。那么此时搜索的内容和商品的属性就是信息量存放的点了。\n", 176 | "对于单次用户，我们只能通过他搜索的和商品实际的属性，以及品类的确定。\n", 177 | "对于多次用户，我们能统计出用户的属性偏好，以及用户的实际想购买的偏好。\n", 178 | "假设现在我任意选取两个属性来演示。\n", 179 | "用下图来表示：true_p1表示p1这个属性在不在item_property_list中，pre_p1表示p1这个属性在不在predict_category_property中" 180 | ] 181 | }, 182 | { 183 | "cell_type": "code", 184 | "execution_count": 14, 185 | "metadata": { 186 | "collapsed": true 187 | }, 188 | "outputs": [], 189 | "source": [ 190 | "dic={\"user_id\":[1,1,1,1,1,2,2,2,2,2],\"true_p1\":[1,1,0,1,1,0,0,0,1,0],\"true_p2\":[0,0,0,1,0,1,1,1,1,1],\"item_id\":[1,2,3,1,2,1,2,1,4,5],\"pre_p1\":[1,1,1,1,1,0,0,1,1,0],\"pre_p2\":[1,0,0,1,0,1,1,0,1,1]}" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 15, 196 | "metadata": { 197 | "collapsed": true 198 | }, 199 | "outputs": [], 200 | "source": [ 201 | "sample=pd.DataFrame(dic)" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": 16, 207 | "metadata": {}, 208 | "outputs": [ 209 | { 210 | "data": { 211 | "text/html": [ 212 | "
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0111101
1210101
2310001
3111111
4210101
5101012
6201012
7110012
8411112
9501012
\n", 331 | "
" 332 | ], 333 | "text/plain": [ 334 | " item_id pre_p1 pre_p2 true_p1 true_p2 user_id\n", 335 | "0 1 1 1 1 0 1\n", 336 | "1 2 1 0 1 0 1\n", 337 | "2 3 1 0 0 0 1\n", 338 | "3 1 1 1 1 1 1\n", 339 | "4 2 1 0 1 0 1\n", 340 | "5 1 0 1 0 1 2\n", 341 | "6 2 0 1 0 1 2\n", 342 | "7 1 1 0 0 1 2\n", 343 | "8 4 1 1 1 1 2\n", 344 | "9 5 0 1 0 1 2" 345 | ] 346 | }, 347 | "execution_count": 16, 348 | "metadata": {}, 349 | "output_type": "execute_result" 350 | } 351 | ], 352 | "source": [ 353 | "sample" 354 | ] 355 | }, 356 | { 357 | "cell_type": "code", 358 | "execution_count": 17, 359 | "metadata": {}, 360 | "outputs": [ 361 | { 362 | "data": { 363 | "text/plain": [ 364 | "user_id\n", 365 | "1 0.8\n", 366 | "2 0.2\n", 367 | "Name: true_p1, dtype: float64" 368 | ] 369 | }, 370 | "execution_count": 17, 371 | "metadata": {}, 372 | "output_type": "execute_result" 373 | } 374 | ], 375 | "source": [ 376 | "sample.groupby(\"user_id\").true_p1.mean()" 377 | ] 378 | }, 379 | { 380 | "cell_type": "code", 381 | "execution_count": 18, 382 | "metadata": {}, 383 | "outputs": [ 384 | { 385 | "data": { 386 | "text/plain": [ 387 | "user_id\n", 388 | "1 1.0\n", 389 | "2 0.4\n", 390 | "Name: pre_p1, dtype: float64" 391 | ] 392 | }, 393 | "execution_count": 18, 394 | "metadata": {}, 395 | "output_type": "execute_result" 396 | } 397 | ], 398 | "source": [ 399 | "sample.groupby(\"user_id\").pre_p1.mean()" 400 | ] 401 | }, 402 | { 403 | "cell_type": "markdown", 404 | "metadata": {}, 405 | "source": [ 406 | "统计出用户对于属性的占比，我们会捕捉到用户对属性的偏好，这是特征的第二次构造。" 407 | ] 408 | }, 409 | { 410 | "cell_type": "code", 411 | "execution_count": 20, 412 | "metadata": { 413 | "collapsed": true 414 | }, 415 | "outputs": [], 416 | "source": [ 417 | "for i in [\"true_p1\",\"true_p2\",\"pre_p1\",\"pre_p2\"]:\n", 418 | " temp=pd.DataFrame(sample.groupby(\"user_id\")[i].mean()).reset_index()\n", 419 | " temp.columns=[\"user_id\",\"%s_mean_user\"%i]\n", 420 | " sample=sample.merge(temp,on=\"user_id\",how=\"left\")" 421 | ] 422 | }, 423 | { 424 | "cell_type": "code", 425 | "execution_count": 21, 426 | "metadata": {}, 427 | "outputs": [ 428 | { 429 | "data": { 430 | "text/html": [ 431 | "
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item_idpre_p1pre_p2true_p1true_p2user_idtrue_p1_mean_usertrue_p2_mean_userpre_p1_mean_userpre_p2_mean_user
01111010.80.21.00.4
12101010.80.21.00.4
23100010.80.21.00.4
31111110.80.21.00.4
42101010.80.21.00.4
51010120.21.00.40.8
62010120.21.00.40.8
71100120.21.00.40.8
84111120.21.00.40.8
95010120.21.00.40.8
\n", 594 | "
" 595 | ], 596 | "text/plain": [ 597 | " item_id pre_p1 pre_p2 true_p1 true_p2 user_id true_p1_mean_user \\\n", 598 | "0 1 1 1 1 0 1 0.8 \n", 599 | "1 2 1 0 1 0 1 0.8 \n", 600 | "2 3 1 0 0 0 1 0.8 \n", 601 | "3 1 1 1 1 1 1 0.8 \n", 602 | "4 2 1 0 1 0 1 0.8 \n", 603 | "5 1 0 1 0 1 2 0.2 \n", 604 | "6 2 0 1 0 1 2 0.2 \n", 605 | "7 1 1 0 0 1 2 0.2 \n", 606 | "8 4 1 1 1 1 2 0.2 \n", 607 | "9 5 0 1 0 1 2 0.2 \n", 608 | "\n", 609 | " true_p2_mean_user pre_p1_mean_user pre_p2_mean_user \n", 610 | "0 0.2 1.0 0.4 \n", 611 | "1 0.2 1.0 0.4 \n", 612 | "2 0.2 1.0 0.4 \n", 613 | "3 0.2 1.0 0.4 \n", 614 | "4 0.2 1.0 0.4 \n", 615 | "5 1.0 0.4 0.8 \n", 616 | "6 1.0 0.4 0.8 \n", 617 | "7 1.0 0.4 0.8 \n", 618 | "8 1.0 0.4 0.8 \n", 619 | "9 1.0 0.4 0.8 " 620 | ] 621 | }, 622 | "execution_count": 21, 623 | "metadata": {}, 624 | "output_type": "execute_result" 625 | } 626 | ], 627 | "source": [ 628 | "sample" 629 | ] 630 | }, 631 | { 632 | "cell_type": "markdown", 633 | "metadata": {}, 634 | "source": [ 635 | "继续，我们可以统计出有这些属性偏好的人和所搜索并点击的item之间的关系" 636 | ] 637 | }, 638 | { 639 | "cell_type": "code", 640 | "execution_count": 23, 641 | "metadata": {}, 642 | "outputs": [ 643 | { 644 | "data": { 645 | "text/plain": [ 646 | "item_id\n", 647 | "1 0.5\n", 648 | "2 0.6\n", 649 | "3 0.8\n", 650 | "4 0.2\n", 651 | "5 0.2\n", 652 | "Name: true_p1_mean_user, dtype: float64" 653 | ] 654 | }, 655 | "execution_count": 23, 656 | "metadata": {}, 657 | "output_type": "execute_result" 658 | } 659 | ], 660 | "source": [ 661 | "sample.groupby(\"item_id\").true_p1_mean_user.mean()" 662 | ] 663 | }, 664 | { 665 | "cell_type": "code", 666 | "execution_count": 24, 667 | "metadata": {}, 668 | "outputs": [ 669 | { 670 | "data": { 671 | "text/plain": [ 672 | "item_id\n", 673 | "1 0.600000\n", 674 | "2 0.466667\n", 675 | "3 0.200000\n", 676 | "4 1.000000\n", 677 | "5 1.000000\n", 678 | "Name: true_p2_mean_user, dtype: float64" 679 | ] 680 | }, 681 | "execution_count": 24, 682 | "metadata": {}, 683 | "output_type": "execute_result" 684 | } 685 | ], 686 | "source": [ 687 | "sample.groupby(\"item_id\").true_p2_mean_user.mean()" 688 | ] 689 | }, 690 | { 691 | "cell_type": "code", 692 | "execution_count": 25, 693 | "metadata": {}, 694 | "outputs": [ 695 | { 696 | "data": { 697 | "text/plain": [ 698 | "item_id\n", 699 | "1 0.7\n", 700 | "2 0.8\n", 701 | "3 1.0\n", 702 | "4 0.4\n", 703 | "5 0.4\n", 704 | "Name: pre_p1_mean_user, dtype: float64" 705 | ] 706 | }, 707 | "execution_count": 25, 708 | "metadata": {}, 709 | "output_type": "execute_result" 710 | } 711 | ], 712 | "source": [ 713 | "sample.groupby(\"item_id\").pre_p1_mean_user.mean()" 714 | ] 715 | }, 716 | { 717 | "cell_type": "code", 718 | "execution_count": 26, 719 | "metadata": {}, 720 | "outputs": [ 721 | { 722 | "data": { 723 | "text/plain": [ 724 | "item_id\n", 725 | "1 0.600000\n", 726 | "2 0.533333\n", 727 | "3 0.400000\n", 728 | "4 0.800000\n", 729 | "5 0.800000\n", 730 | "Name: pre_p2_mean_user, dtype: float64" 731 | ] 732 | }, 733 | "execution_count": 26, 734 | "metadata": {}, 735 | "output_type": "execute_result" 736 | } 737 | ], 738 | "source": [ 739 | "sample.groupby(\"item_id\").pre_p2_mean_user.mean()" 740 | ] 741 | }, 742 | { 743 | "cell_type": "markdown", 744 | "metadata": { 745 | "collapsed": true 746 | }, 747 | "source": [ 748 | "所以，我们又可以构造出查看该item的人对不同的属性的平均偏好，这是特征的第三次构造。" 749 | ] 750 | }, 751 | { 752 | "cell_type": "markdown", 753 | "metadata": { 754 | "collapsed": true 755 | }, 756 | "source": [ 757 | "总而言之一句话：用属性来表征user_id,用user_id来表征item_id。" 758 | ] 759 | }, 760 | { 761 | "cell_type": "code", 762 | "execution_count": null, 763 | "metadata": { 764 | "collapsed": true 765 | }, 766 | "outputs": [], 767 | "source": [] 768 | }, 769 | { 770 | "cell_type": "markdown", 771 | "metadata": { 772 | "collapsed": true 773 | }, 774 | "source": [ 775 | "最终再加上user_id对于item_id,cate_id等的时间差特征，排序特征和计数特征等，共同构成了我的特征集。\n", 776 | "本题核心就是根据数据的分析选用合适的数据建模，围绕着user_id和item_id构造特征，找出user_id和item_id的数据表达方法，从数据的痕迹里复现当时用户的场景。" 777 | ] 778 | }, 779 | { 780 | "cell_type": "code", 781 | "execution_count": null, 782 | "metadata": { 783 | "collapsed": true 784 | }, 785 | "outputs": [], 786 | "source": [] 787 | }, 788 | { 789 | "cell_type": "code", 790 | "execution_count": null, 791 | "metadata": { 792 | "collapsed": true 793 | }, 794 | "outputs": [], 795 | "source": [] 796 | }, 797 | { 798 | "cell_type": "code", 799 | "execution_count": null, 800 | "metadata": { 801 | "collapsed": true 802 | }, 803 | "outputs": [], 804 | "source": [] 805 | } 806 | ], 807 | "metadata": { 808 | "kernelspec": { 809 | "display_name": "Python 2", 810 | "language": "python", 811 | "name": "python2" 812 | }, 813 | "language_info": { 814 | "codemirror_mode": { 815 | "name": "ipython", 816 | "version": 2 817 | }, 818 | "file_extension": ".py", 819 | "mimetype": "text/x-python", 820 | "name": "python", 821 | "nbconvert_exporter": "python", 822 | "pygments_lexer": "ipython2", 823 | "version": "2.7.13" 824 | } 825 | }, 826 | "nbformat": 4, 827 | "nbformat_minor": 2 828 | } 829 | --------------------------------------------------------------------------------