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    "path": "CONTRIBUTORS.md",
    "content": "# 贡献者名单\n\n> 第一期 (2018-01-01)\n\n* [@片刻](https://github.com/jiangzhonglian)\n* [@那伊抹微笑](https://github.com/wangyangting)\n* [@瑶妹](https://github.com/chenyyx)\n* [@loveSnowBest](https://github.com/zehuichen123)\n* [@谈笑风生](https://github.com/zhu1040028623)\n* [@诺木人](https://github.com/1mrliu)\n* [@飞龙](https://github.com/wizardforcel)\n\n> 第二期 (2018-06-01)\n\n* [@KrisYu](https://github.com/KrisYu/LeetCode-CLRS-Python)\n* 授权信息: <https://github.com/apachecn/Interview/tree/master/docs/Algorithm/ProjectCornerstone/ApproveLetter.md>\n* [@Lisanaaa](https://github.com/Lisanaaa) -- 由于个人商业化原因，退出\n* [@片刻](https://github.com/jiangzhonglian) - [@小瑶](https://github.com/chenyyx) - [@cclauss](https://github.com/cclauss) - [@yudaer](https://github.com/yudaer)\n* [@yuzhoujr](https://github.com/yuzhoujr) - [@wizardforcel](https://github.com/wizardforcel) - [@Stuming](https://github.com/Stuming) - [@GaofanHu](https://github.com/GaofanHu)\n* [@er3456qi](https://github.com/er3456qi) - [@xshahq](https://github.com/xshahq) - [@xiaqunfeng](https://github.com/xiaqunfeng) - [@CaviarChen](https://github.com/CaviarChen)\n* [@royIdoodle](https://github.com/royIdoodle) - [@MarsXue](https://github.com/MarsXue) - [@nature1995](https://github.com/nature1995)\n\n> 第三期 (2019-07-17)\n\n* [@片刻](https://github.com/jiangzhonglian)\n* [@飞龙](https://github.com/wizardforcel)\n* [@xixici](https://github.com/xixici)\n* [@royIdoodle](https://github.com/royIdoodle)\n"
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    "path": "Dockerfile",
    "content": "FROM httpd:2.4\nCOPY ./ /usr/local/apache2/htdocs/"
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    "path": "LICENSE",
    "content": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0)\n\nCopyright © 2020 ApacheCN(apachecn@163.com)\n\nBy exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (\"Public License\"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions.\n\nSection 1 – Definitions.\n\na.  Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.\nb.  Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License.\nc.  BY-NC-SA Compatible License means a license listed at creativecommons.org/compatiblelicenses, approved by Creative Commons as essentially the equivalent of this Public License.\nd.  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In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes.\n\nSection 3 – License Conditions.\n\nYour exercise of the Licensed Rights is expressly made subject to the following conditions.\n\na.  Attribution.\n    1.  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You may satisfy this condition in any reasonable manner based on the medium, means, and context in which You Share Adapted Material.\n    3.  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If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.\nc.  No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.\nd.  Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority."
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    "path": "README.md",
    "content": "<p align=\"center\">\n    <a href=\"https://www.apachecn.org\">\n        <img width=\"200\" src=\"http://data.apachecn.org/img/logo.jpg\">\n    </a>\n    <br >\n    <a href=\"https://www.apachecn.org/\"><img src=\"https://img.shields.io/badge/%3E-HOME-green.svg\"></a>\n    <a href=\"http://home.apachecn.org/about/\"><img src=\"https://img.shields.io/badge/%3E-ABOUT-green.svg\"></a>\n    <a href=\"mailto:apache@163.com\"><img src=\"https://img.shields.io/badge/%3E-Email-green.svg\"></a>\n</p>\n\n<h1 align=\"center\">Interview——IT 行业应试学知识库</h1>\n\n\n> 程序员的双手是魔术师的双手，他们把枯燥无味的代码变成了丰富多彩的软件。——《疯狂的程序员》\n\n## 在线阅读\n\n* 网址: https://interview.apachecn.org\n\n## **协议**\n\n[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh)\n\n## 赞助我们\n\n<img src=\"http://data.apachecn.org/img/about/donate.jpg\" alt=\"微信&支付宝\" />\n\n---\n\n<!-- \n> 特别赞助商(欢迎“私聊”赞助)\n\n<table>\n      <tbody>\n        <tr>\n          <td align=\"center\" valign=\"middle\">\n            <a href=\"https://coding.net/?utm_source=ApacheCN&utm_medium=banner&utm_campaign=march2019\" target=\"_blank\">\n              <img width=\"1080\" src=\"http://data.apachecn.org/img/SpecialSponsors/CodingNet.png\">\n            </a>\n          </td>\n      </tbody>\n</table> \n-->\n"
  },
  {
    "path": "SUMMARY.md",
    "content": "+   [Introduction](README.md)\n+   面试求职\n    +   [简历](docs/面试求职/简历.md)\n    +   [简历范文](docs/面试求职/简历范文.md)\n    +   [学历](docs/面试求职/学历.md)\n    +   [刷题](docs/面试求职/刷题.md)\n    +   [公司](docs/面试求职/公司.md)\n    +   [职场](docs/面试求职/职场.md)\n    +   [理财](docs/面试求职/理财.md)\n    +   [年龄](docs/面试求职/年龄.md)\n+   [算法刷题](docs/Algorithm/README.md)\n    +   [数据结构](docs/Algorithm/DataStructure/README.md)\n    +   [LeetCode C++ 版本↗](https://algo.apachecn.org/#/docs/leetcode/cpp/README) \n    +   [LeetCode Java 版本↗](https://algo.apachecn.org/#/docs/leetcode/java/README) \n    +   [LeetCode Python 版本↗](https://algo.apachecn.org/#/docs/leetcode/python/README)\n    +   [LeetCode JavaScript 版本↗](https://algo.apachecn.org/#/docs/leetcode/javascript/README)\n    +   [剑指offer↗](https://algo.apachecn.org/#/docs/jianzhioffer/java/README)\n+   [IT 八股文↗](https://bgww.apachecn.org/#/)\n+   [Kaggle比赛](docs/Kaggle/README.md)\n    +   [Kernel 备份（一）↗](https://github.com/it-ebooks-0/kaggle-kernel-pt1)\n    +   [Kernel 备份（二）↗](https://github.com/it-ebooks-0/kaggle-kernel-pt2)\n    +   [Kernel 备份（三）↗](https://github.com/it-ebooks-0/kaggle-kernel-pt3)\n    +   [Kernel 备份（四）↗](https://github.com/it-ebooks-0/kaggle-kernel-pt4)\n+   职业认证\n    +    [AQF↗](https://github.com/apachecn/interview-books/tree/master/AQF)\n    +    [CCNA/NP/IE↗](https://github.com/apachecn/interview-books/tree/master/CCNA-NP-IE)\n    +    [CEH↗](https://github.com/apachecn/interview-books/tree/master/CEH)\n    +    [PMP↗](https://github.com/apachecn/interview-books/tree/master/PMP)\n+    [职位数据库↗](https://github.com/apachecn/interview-books/tree/master/%E8%81%8C%E4%BD%8D%E6%95%B0%E6%8D%AE%E5%BA%93)\n+    [面试译文集↗](https://itvw.apachecn.org)\n+    [牛客面试题库↗](https://github.com/apachecn/interview-books/tree/master/NowCoder)\n+    [GitHub快速入门](docs/GitHub/README.md)\n+    [导师评价网备份↗](https://rms.apachecn.org/#/)\n+    [校招污点公司记录↗](https://github.com/ShameCom/ShameCom)\n+    [独立开发/自由职业/远程工作/数字游民知识库↗](https://idw.apachecn.org/#/)\n+    [贡献者名单](CONTRIBUTORS.md)\n\n"
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n},e.formatUnsupportedCmd=function(t){for(var e=[],r=0;r<t.length;r++)e.push({type:\"textord\",mode:\"text\",text:t[r]});var a={type:\"text\",mode:this.mode,body:e};return{type:\"color\",mode:this.mode,color:this.settings.errorColor,body:[a]}},e.parseAtom=function(t){var e,r,a=this.parseGroup(\"atom\",!1,null,t);if(\"text\"===this.mode)return a;for(;;){this.consumeSpaces();var n=this.fetch();if(\"\\\\limits\"===n.text||\"\\\\nolimits\"===n.text){var i=Vt(a,\"op\");if(i){var s=\"\\\\limits\"===n.text;i.limits=s,i.alwaysHandleSupSub=!0}else{if(!(i=Vt(a,\"operatorname\"))||!i.alwaysHandleSupSub)throw new o(\"Limit controls must follow a math operator\",n);var h=\"\\\\limits\"===n.text;i.limits=h}this.consume()}else if(\"^\"===n.text){if(e)throw new o(\"Double superscript\",n);e=this.handleSupSubscript(\"superscript\")}else if(\"_\"===n.text){if(r)throw new o(\"Double subscript\",n);r=this.handleSupSubscript(\"subscript\")}else{if(\"'\"!==n.text)break;if(e)throw new o(\"Double 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o(\"No function handler for \"+t)},e.parseArguments=function(t,e){var r=e.numArgs+e.numOptionalArgs;if(0===r)return{args:[],optArgs:[]};for(var a=e.greediness,n=[],i=[],s=0;s<r;s++){var h=e.argTypes&&e.argTypes[s],l=s<e.numOptionalArgs,m=s>0&&!l||0===s&&!l&&\"math\"===this.mode,c=this.parseGroupOfType(\"argument to '\"+t+\"'\",h,l,a,m);if(!c){if(l){i.push(null);continue}throw new o(\"Expected group after '\"+t+\"'\",this.fetch())}(l?i:n).push(c)}return{args:n,optArgs:i}},e.parseGroupOfType=function(t,e,r,a,n){switch(e){case\"color\":return n&&this.consumeSpaces(),this.parseColorGroup(r);case\"size\":return n&&this.consumeSpaces(),this.parseSizeGroup(r);case\"url\":return this.parseUrlGroup(r,n);case\"math\":case\"text\":return this.parseGroup(t,r,a,void 0,e,n);case\"hbox\":var i=this.parseGroup(t,r,a,void 0,\"text\",n);return i?{type:\"styling\",mode:i.mode,body:[i],style:\"text\"}:i;case\"raw\":if(n&&this.consumeSpaces(),r&&\"{\"===this.fetch().text)return null;var 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a,n=this.fetch(),i=n,s=\"\";\"EOF\"!==(a=this.fetch()).text&&t.test(s+a.text);)s+=(i=a).text,this.consume();if(\"\"===s)throw new o(\"Invalid \"+e+\": '\"+n.text+\"'\",n);return this.mode=r,n.range(i,s)},e.parseColorGroup=function(t){var e=this.parseStringGroup(\"color\",t);if(!e)return null;var r=/^(#[a-f0-9]{3}|#?[a-f0-9]{6}|[a-z]+)$/i.exec(e.text);if(!r)throw new o(\"Invalid color: '\"+e.text+\"'\",e);var a=r[0];return/^[0-9a-f]{6}$/i.test(a)&&(a=\"#\"+a),{type:\"color-token\",mode:this.mode,color:a}},e.parseSizeGroup=function(t){var e,r=!1;if(!(e=t||\"{\"===this.fetch().text?this.parseStringGroup(\"size\",t):this.parseRegexGroup(/^[-+]? *(?:$|\\d+|\\d+\\.\\d*|\\.\\d*) *[a-z]{0,2} *$/,\"size\")))return null;t||0!==e.text.length||(e.text=\"0pt\",r=!0);var a=/([-+]?) *(\\d+(?:\\.\\d*)?|\\.\\d+) *([a-z]{2})/.exec(e.text);if(!a)throw new o(\"Invalid size: '\"+e.text+\"'\",e);var n={number:+(a[1]+a[2]),unit:a[3]};if(!At(n))throw new o(\"Invalid unit: 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    "path": "docs/Algorithm/DataStructure/JavaScript.md",
    "content": "# 十大排序算法集合\n\n![](https://user-gold-cdn.xitu.io/2016/11/29/4abde1748817d7f35f2bf8b6a058aa40?imageView2/0/w/1280/h/960/format/webp/ignore-error/1)\n\n## 冒泡排序\n通过相邻元素的比较和交换，使得每一趟循环都能找到未有序数组的最大值或最小值。   \n\n最好：`O(n)`，只需要冒泡一次数组就有序了。   \n最坏：`O(n²)`   \n平均：`O(n²)`\n```\nfunction bubbleSort(nums) {\n  for(let i=0, len=nums.length; i<len-1; i++) {\n    // 如果一轮比较中没有需要交换的数据，则说明数组已经有序。主要是对[5,1,2,3,4]之类的数组进行优化\n    let mark = true;\n    for(let j=0; j<len-i-1; j++) {\n      if(nums[j] > nums[j+1]) {\n        [nums[j], nums[j+1]] = [nums[j+1], nums[j]];\n        mark = false;\n      }\n    }\n    if(mark)  return;\n  }\n}\n```\n\n## 双向冒泡\n普通的冒泡排序在一趟循环中只能找出一个最大值或最小值，双向冒泡则是多一轮循环既找出最大值也找出最小值。\n```\nfunction bubbleSort_twoWays(nums) {\n  let low = 0;\n  let high = nums.length - 1;\n  while(low < high) {\n    let mark = true;\n    // 找到最大值放到右边\n    for(let i=low; i<high; i++) {\n      if(nums[i] > nums[i+1]) {\n        [nums[i], nums[i+1]] = [nums[i+1], nums[i]];\n        mark = false;\n      }\n    }\n    high--;\n    // 找到最小值放到左边\n    for(let j=high; j>low; j--) {\n      if(nums[j] < nums[j-1]) {\n        [nums[j], nums[j-1]] = [nums[j-1], nums[j]];\n        mark = false;\n      }\n    }\n    low++;\n    if(mark)  return;\n  }\n}\n```\n\n## 选择排序\n和冒泡排序相似，区别在于选择排序是将每一个元素和它后面的元素进行比较和交换。  \n\n最好：`O(n²)`   \n最坏：`O(n²)`   \n平均：`O(n²)`\n```\nfunction selectSort(nums) {\n  for(let i=0, len=nums.length; i<len; i++) {\n    for(let j=i+1; j<len; j++) {\n      if(nums[i] > nums[j]) {\n        [nums[i], nums[j]] = [nums[j], nums[i]];\n      }\n    }\n  }\n}\n```\n\n## 插入排序\n以第一个元素作为有序数组，其后的元素通过在这个已有序的数组中找到合适的位置并插入。\n\n最好：`O(n)`，原数组已经是升序的。     \n最坏：`O(n²)`    \n平均：`O(n²)`\n```\nfunction insertSort(nums) {\n  for(let i=1, len=nums.length; i<len; i++) {\n    let temp = nums[i];\n    let j = i;\n    while(j >= 0 && temp < nums[j-1]) {\n      nums[j] = nums[j-1];\n      j--;\n    }\n    nums[j] = temp;\n  }\n}\n```\n\n## 快速排序\n选择一个元素作为基数（通常是第一个元素），把比基数小的元素放到它左边，比基数大的元素放到它右边（相当于二分），再不断递归基数左右两边的序列。   \n\n最好：`O(n * logn)`，所有数均匀分布在基数的两边，此时的递归就是不断地二分左右序列。  \n最坏：`O(n²)` ，所有数都分布在基数的一边，此时划分左右序列就相当于是插入排序。   \n平均：`O(n * logn)`    \n\n参考学习链接：    \n[算法 3：最常用的排序——快速排序](https://wiki.jikexueyuan.com/project/easy-learn-algorithm/fast-sort.html)     \n[三种快速排序以及快速排序的优化](https://blog.csdn.net/insistGoGo/article/details/7785038)\n### 快速排序之填坑\n从右边向中间推进的时候，遇到小于基数的数就赋给左边（一开始是基数的位置），右边保留原先的值等之后被左边的值填上。\n```\nfunction quickSort(nums) {\n  // 递归排序基数左右两边的序列\n  function recursive(arr, left, right) {\n    if(left >= right)  return;\n    let index = partition(arr, left, right);\n    recursive(arr, left, index - 1);\n    recursive(arr, index + 1, right);\n    return arr;\n  }\n  // 将小于基数的数放到基数左边，大于基数的数放到基数右边，并返回基数的位置\n  function partition(arr, left, right) {\n    // 取第一个数为基数\n    let temp = arr[left];\n    while(left < right) {\n      while(left < right && arr[right] >= temp)  right--;\n      arr[left] = arr[right];\n      while(left < right && arr[left] < temp)  left++;\n      arr[right] = arr[left];\n    }\n    // 修改基数的位置\n    arr[left] = temp;\n    return left;\n  }\n  recursive(nums, 0, nums.length-1);\n}\n```\n\n### 快速排序之交换\n从左右两边向中间推进的时候，遇到不符合的数就两边交换值。\n```\nfunction quickSort1(nums) {\n  function recursive(arr, left, right) {\n    if(left >= right)  return;\n    let index = partition(arr, left, right);\n    recursive(arr, left, index - 1);\n    recursive(arr, index + 1, right);\n    return arr;\n  }\n  function partition(arr, left, right) {\n    let temp = arr[left];\n    let p = left + 1;\n    let q = right;\n    while(p <= q) {\n      while(p <= q && arr[p] < temp)  p++;\n      while(p <= q && arr[q] > temp)  q--;\n      if(p <= q) {\n        [arr[p], arr[q]] = [arr[q], arr[p]];\n        // 交换值后两边各向中间推进一位\n        p++;\n        q--;\n      }\n    }\n    // 修改基数的位置\n    [arr[left], arr[q]] = [arr[q], arr[left]];\n    return q;\n  }\n  recursive(nums, 0, nums.length-1);\n}\n```\n\n## 归并排序\n递归将数组分为两个序列，有序合并这两个序列。   \n\n最好：`O(n * logn)`    \n最坏：`O(n * logn)`   \n平均：`O(n * logn)`  \n\n参考学习链接：   \n[图解排序算法(四)之归并排序](https://www.cnblogs.com/chengxiao/p/6194356.html)\n```\nfunction mergeSort(nums) {\n  // 有序合并两个数组\n  function merge(l1, r1, l2, r2) {\n    let arr = [];\n    let index = 0;\n    let i = l1, j = l2;\n    while(i <= r1 && j <= r2) {\n      arr[index++] = nums[i] < nums[j] ? nums[i++] : nums[j++];\n    }\n    while(i <= r1)  arr[index++] = nums[i++];\n    while(j <= r2)  arr[index++] = nums[j++];\n    // 将有序合并后的数组修改回原数组\n    for(let t=0; t<index; t++) {\n      nums[l1 + t] = arr[t];\n    }\n  }\n  // 递归将数组分为两个序列\n  function recursive(left, right) {\n    if(left >= right)  return;\n    // 比起(left+right)/2，更推荐下面这种写法，可以避免数溢出\n    let mid = parseInt((right - left) / 2) + left;\n    recursive(left, mid);\n    recursive(mid+1, right);\n    merge(left, mid, mid+1, right);\n    return nums;\n  }\n  recursive(0, nums.length-1);\n}\n```\n\n## 桶排序\n取 n 个桶，根据数组的最大值和最小值确认每个桶存放的数的区间，将数组元素插入到相应的桶里，最后再合并各个桶。   \n\n最好：`O(n)`，每个数都在分布在一个桶里，这样就不用将数插入排序到桶里了(类似于计数排序以空间换时间)。    \n最坏：`O(n²)`，所有的数都分布在一个桶里。    \n平均：`O(n + k)`，k表示桶的个数。   \n\n参考学习链接：   \n[拜托，面试别再问我桶排序了！！！](http://zhuanlan.51cto.com/art/201811/586129.htm)\n```\nfunction bucketSort(nums) {\n  // 桶的个数，只要是正数即可\n  let num = 5;\n  let max = Math.max(...nums);\n  let min = Math.min(...nums);\n  // 计算每个桶存放的数值范围，至少为1，\n  let range = Math.ceil((max - min) / num) || 1;\n  // 创建二维数组，第一维表示第几个桶，第二维表示该桶里存放的数\n  let arr = Array.from(Array(num)).map(() => Array().fill(0));\n  nums.forEach(val => {\n    // 计算元素应该分布在哪个桶\n    let index = parseInt((val - min) / range);\n    // 防止index越界，例如当[5,1,1,2,0,0]时index会出现5\n    index = index >= num ? num - 1 : index;\n    let temp = arr[index];\n    // 插入排序，将元素有序插入到桶中\n    let j = temp.length - 1;\n    while(j >= 0 && val < temp[j]) {\n      temp[j+1] = temp[j];\n      j--;\n    }\n    temp[j+1] = val;\n  })\n  // 修改回原数组\n  let res = [].concat.apply([], arr);\n  nums.forEach((val, i) => {\n    nums[i] = res[i];\n  })\n}\n```\n\n## 基数排序\n使用十个桶 0-9，把每个数从低位到高位根据位数放到相应的桶里，以此循环最大值的位数次。**但只能排列正整数，因为遇到负号和小数点无法进行比较**。   \n\n最好：`O(n * k)`，k表示最大值的位数。   \n最坏：`O(n * k)`   \n平均：`O(n * k)`   \n\n参考学习链接：    \n[算法总结系列之五: 基数排序(Radix Sort)](https://www.cnblogs.com/sun/archive/2008/06/26/1230095.html)\n[]()\n```\nfunction radixSort(nums) {\n  // 计算位数\n  function getDigits(n) {\n    let sum = 0;\n    while(n) {\n      sum++;\n      n = parseInt(n / 10);\n    }\n    return sum;\n  }\n  // 第一维表示位数即0-9，第二维表示里面存放的值\n  let arr = Array.from(Array(10)).map(() => Array());\n  let max = Math.max(...nums);\n  let maxDigits = getDigits(max);\n  for(let i=0, len=nums.length; i<len; i++) {\n    // 用0把每一个数都填充成相同的位数\n    nums[i] = (nums[i] + '').padStart(maxDigits, 0);\n    // 先根据个位数把每一个数放到相应的桶里\n    let temp = nums[i][nums[i].length-1];\n    arr[temp].push(nums[i]);\n  }\n  // 循环判断每个位数\n  for(let i=maxDigits-2; i>=0; i--) {\n    // 循环每一个桶\n    for(let j=0; j<=9; j++) {\n      let temp = arr[j]\n      let len = temp.length;\n      // 根据当前的位数i把桶里的数放到相应的桶里\n      while(len--) {\n        let str = temp[0];\n        temp.shift();\n        arr[str[i]].push(str);\n      }\n    }\n  }\n  // 修改回原数组\n  let res = [].concat.apply([], arr);\n  nums.forEach((val, index) => {\n    nums[index] = +res[index];\n  }) \n}\n```\n\n## 计数排序\n以数组元素值为键，出现次数为值存进一个临时数组，最后再遍历这个临时数组还原回原数组。因为 JavaScript 的数组下标是以字符串形式存储的，所以**计数排序可以用来排列负数，但不可以排列小数**。   \n\n最好：`O(n + k)`，k是最大值和最小值的差。   \n最坏：`O(n + k)`   \n平均：`O(n + k)`\n```\nfunction countingSort(nums) {\n  let arr = [];\n  let max = Math.max(...nums);\n  let min = Math.min(...nums);\n  // 装桶\n  for(let i=0, len=nums.length; i<len; i++) {\n    let temp = nums[i];\n    arr[temp] = arr[temp] + 1 || 1;\n  }\n  let index = 0;\n  // 还原原数组\n  for(let i=min; i<=max; i++) {\n    while(arr[i] > 0) {\n      nums[index++] = i;\n      arr[i]--;\n    }\n  }\n}\n```\n\n## 计数排序优化\n把每一个数组元素都加上 min 的相反数，来避免特殊情况下的空间浪费，通过这种优化可以把所开的空间大小从 max+1 降低为 max-min+1，max 和 min 分别为数组中的最大值和最小值。   \n\n比如数组 [103, 102, 101, 100]，普通的计数排序需要开一个长度为 104 的数组，而且前面 100 个值都是 undefined，使用该优化方法后可以只开一个长度为 4 的数组。\n```\nfunction countingSort(nums) {\n  let arr = [];\n  let max = Math.max(...nums);\n  let min = Math.min(...nums);\n  // 加上最小值的相反数来缩小数组范围\n  let add = -min;\n  for(let i=0, len=nums.length; i<len; i++) {\n    let temp = nums[i];\n    temp += add;\n    arr[temp] = arr[temp] + 1 || 1;\n  }\n  let index = 0;\n  for(let i=min; i<=max; i++) {\n    let temp = arr[i+add];\n    while(temp > 0) {\n      nums[index++] = i;\n      temp--;\n    }\n  }\n}\n```\n\n## 堆排序\n根据数组建立一个堆（类似完全二叉树），每个结点的值都大于左右结点（最大堆，通常用于升序），或小于左右结点（最小堆，通常用于降序）。对于升序排序，先构建最大堆后，交换堆顶元素（表示最大值）和堆底元素，每一次交换都能得到未有序序列的最大值。重新调整最大堆，再交换堆顶元素和堆底元素，重复 n-1 次后就能得到一个升序的数组。  \n\n最好：`O(n * logn)`，logn是调整最大堆所花的时间。   \n最坏：`O(n * logn)`   \n平均：`O(n * logn)`   \n\n参考学习链接：    \n[常见排序算法 - 堆排序 (Heap Sort)](http://bubkoo.com/2014/01/14/sort-algorithm/heap-sort/)    \n[图解排序算法(三)之堆排序](https://www.cnblogs.com/chengxiao/p/6129630.html)\n```\nfunction heapSort(nums) {\n  // 调整最大堆，使index的值大于左右节点\n  function adjustHeap(nums, index, size) {\n    // 交换后可能会破坏堆结构，需要循环使得每一个父节点都大于左右结点\n    while(true) {\n      let max = index;\n      let left = index * 2 + 1;   // 左节点\n      let right = index * 2 + 2;  // 右节点\n      if(left < size && nums[max] < nums[left])  max = left;\n      if(right < size && nums[max] < nums[right])  max = right;\n      // 如果左右结点大于当前的结点则交换，并再循环一遍判断交换后的左右结点位置是否破坏了堆结构（比左右结点小了）\n      if(index !== max) {\n        [nums[index], nums[max]] = [nums[max], nums[index]];\n        index = max;\n      }\n      else {\n        break;\n      }\n    }\n  }\n  // 建立最大堆\n  function buildHeap(nums) {\n    // 注意这里的头节点是从0开始的，所以最后一个非叶子结点是 parseInt(nums.length/2)-1\n    let start = parseInt(nums.length / 2) - 1;\n    let size = nums.length;\n    // 从最后一个非叶子结点开始调整，直至堆顶。\n    for(let i=start; i>=0; i--) {\n      adjustHeap(nums, i, size);\n    }\n  }\n\n  buildHeap(nums);\n  // 循环n-1次，每次循环后交换堆顶元素和堆底元素并重新调整堆结构\n  for(let i=nums.length-1; i>0; i--) {\n    [nums[i], nums[0]] = [nums[0], nums[i]];\n    adjustHeap(nums, 0, i);\n  }\n}\n```\n\n## 希尔排序\n通过某个增量 gap，将整个序列分给若干组，从后往前进行组内成员的比较和交换，随后逐步缩小增量至 1。希尔排序类似于插入排序，只是一开始向前移动的步数从 1 变成了 gap。 \n\n最好：`O(n * logn)`，步长不断二分。    \n最坏：`O(n * logn)`   \n平均：`O(n * logn)`   \n\n参考学习链接：   \n[图解排序算法(二)之希尔排序](https://www.cnblogs.com/chengxiao/p/6104371.html)\n```\nfunction shellSort(nums) {\n  let len = nums.length;\n  // 初始步数\n  let gap = parseInt(len / 2);\n  // 逐渐缩小步数\n  while(gap) {\n    // 从第gap个元素开始遍历\n    for(let i=gap; i<len; i++) {\n      // 逐步其和前面其他的组成员进行比较和交换\n      for(let j=i-gap; j>=0; j-=gap) {\n        if(nums[j] > nums[j+gap]) {\n          [nums[j], nums[j+gap]] = [nums[j+gap], nums[j]];\n        }\n        else {\n          break;\n        }\n      }\n    }\n    gap = parseInt(gap / 2);\n  }\n}\n```\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Others/NextNodeInOrderTree.md",
    "content": "``` python\n       8\n      /\n     5\n    / \\\n   3   6\n  / \\   \\\n 1  4    7\n\n\nclass Node {\n  Node parent, lc, rc;\n  int val;\n}\n```\n\n1. 首先判断其自有无右孩子，若有，则取其右子树的最左节点; 若无，则开始2\n\n\n2. 它是其父亲节点的左孩子，则其父亲节点\n\n\n2. 它是其父亲节点的右孩子，则从其父亲开始往上追溯到第一个向右的节点，如果没有这个节点或者说没有父亲节点，则无下一个节点，若有则取之\n                                      \n\n```python\ndef nextNode(node):\n    def leftest(node):\n        while node.lc:\n            node = node.lc\n        return node\n    if node.rc:\n        return leftest(node.rc)\n    if not node.parent:\n        return None\n    if node == node.parent.lc:\n        return node.parent\n    elif node == node.parent.rc:\n        while node.parent.parent:\n            if node.parent != node.parent.parent.lc:\n                node = node.parent\n            else:\n                return node.parent.parent\n        return None\n```\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Others/TopKWords.java",
    "content": "import java.io.*;\nimport java.util.*;\n\n/* display the most frequent K words in the file and the times it appear\n    in the file – shown in order (ignore case and periods) */\n\npublic class TopKWords {\n    static class CountWords {\n        private String fileName;\n\n        public CountWords(String fileName) {\n            this.fileName = fileName;\n        }\n\n        public Map<String, Integer> getDictionary() {\n            Map<String, Integer> dictionary = new HashMap<>();\n            FileInputStream fis = null;\n\n            try {\n\n                fis = new FileInputStream(fileName);  // open the file\n                int in = 0;\n                String s = \"\";  // init a empty word\n                in = fis.read();  // read one character\n\n                while (-1 != in) {\n                    if (Character.isLetter((char)in)) {\n                        s += (char)in;  //if get a letter, append to s\n                    } else {\n                        // this branch means an entire word has just been read\n                        if (s.length() > 0) {\n                            // see whether word exists or not\n                            if (dictionary.containsKey(s)) {\n                                // if exist, count++\n                                dictionary.put(s, dictionary.get(s) + 1);\n                            } else {\n                                // if not exist, initiate count of this word with 1\n                                dictionary.put(s, 1);\n                            }\n                        }\n                        s = \"\"; // reInit a empty word\n                    }\n                    in = fis.read();\n                }\n                return dictionary;\n            } catch (IOException e) {\n                e.printStackTrace();\n            } finally {\n                try {\n                    // you always have to close the I/O streams\n                    fis.close();\n                } catch (IOException e) {\n                    e.printStackTrace();\n                }\n            }\n            return null;\n        }\n    }\n    public static void main(String[] args) {\n        // you can replace the filePath with yours, e.g.\n        // CountWords cw = new CountWords(\"/Users/lisanaaa/Desktop/words.txt\");\n        CountWords cw = new CountWords(\"/words.txt\");\n        Map<String, Integer> dictionary = cw.getDictionary();  // get the words dictionary: {word: frequency}\n\n        // we change the map to list for convenient sort\n        List<Map.Entry<String, Integer>> list = new ArrayList<>(dictionary.entrySet());\n\n        // sort by lambda valueComparator\n        list.sort(Comparator.comparing(\n                m -> m.getValue())\n        );\n\n        Scanner input = new Scanner(System.in);\n        int k = input.nextInt();\n        while (k > list.size()) {\n            System.out.println(\"Retype a number, your number is too large\");\n            input = new Scanner(System.in);\n            k = input.nextInt();\n        }\n        for (int i = 0; i < k; i++) {\n            System.out.println(list.get(list.size() - i - 1));\n        }\n    }\n}\n\n\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Others/words.txt",
    "content": "\n\nLorem ipsum dolor sit amet, consectetur adipiscing elit. Nam non pharetra purus. Quisque non tristique risus. Nulla ultricies eget nunc a volutpat. Aliquam semper, eros sit amet semper pellentesque, elit magna auctor ligula, sit amet vulputate nunc arcu sit amet ipsum. Aenean efficitur, felis ut tincidunt semper, sem sapien facilisis felis, et elementum ipsum risus sit amet metus. Pellentesque et orci at odio hendrerit lobortis vitae non felis. Phasellus nunc eros, ultricies sed dolor vel, ultricies mattis neque. Mauris placerat fringilla libero id efficitur. Donec nec imperdiet augue. Sed sit amet risus in turpis scelerisque rutrum. Ut sed pretium dolor. Donec metus nisl, blandit quis augue et, interdum suscipit metus. Morbi rutrum eros vel lacus aliquet, et maximus erat dapibus. Vivamus est justo, sagittis a augue id, vulputate vehicula nunc. Praesent mattis eros sapien, ac sagittis urna accumsan sit amet. Proin non dui tincidunt, tristique nisi in, vestibulum lorem.\n\nQuisque fermentum justo lacus, sit amet tempus lectus congue eu. Nulla sed quam nec nulla consequat tincidunt. Sed sed nunc diam. Integer ex ante, accumsan id fermentum a, interdum eu sapien. Aliquam justo dui, luctus vel ligula in, lacinia ornare turpis. Praesent leo purus, fringilla ut lobortis et, porta ac urna. Mauris id velit porta, iaculis felis non, sagittis nunc. Quisque non condimentum nisl, vitae venenatis urna. Nam commodo euismod felis, ac efficitur turpis scelerisque nec. Phasellus sagittis nec lacus eu bibendum. Suspendisse finibus vestibulum quam, quis volutpat ante. Duis nibh ligula, dapibus at est sed, tincidunt convallis augue. Pellentesque non consequat mi. Curabitur consequat imperdiet efficitur.\n\nMauris ipsum arcu, fermentum in urna ultricies, venenatis vehicula nisl. Donec viverra non tellus sit amet porta. Phasellus ornare magna eget condimentum mollis. In hac habitasse platea dictumst. Proin in nibh venenatis, fermentum neque nec, commodo urna. In eget condimentum risus, ac interdum dolor. Sed ut neque sapien. Proin nulla diam, lobortis sed ultrices eget, blandit ut libero.\n\nFusce at varius dui. Quisque viverra vulputate consectetur. Quisque sagittis id ante a vestibulum. Phasellus vel lobortis lectus. Duis vestibulum, quam vel congue elementum, lacus nibh efficitur odio, consectetur dapibus ipsum velit at diam. Duis eu nunc id diam tempor vestibulum sed luctus arcu. Nunc eu luctus ex. Morbi et commodo eros, non suscipit enim. Ut fringilla odio nec tincidunt scelerisque. Nam quis elit cursus, ullamcorper lorem id, convallis dui. Mauris elementum tortor odio, nec imperdiet nisl bibendum eget. Suspendisse potenti.\n\nCras ut efficitur enim. Sed consequat non ante id euismod. Ut at magna viverra, aliquam purus a, lobortis mi. Donec hendrerit odio eu nunc imperdiet, quis pharetra sapien volutpat. Morbi leo libero, egestas vitae tortor eget, dictum volutpat augue. Donec arcu lacus, tristique eu posuere ac, pharetra vel ante. Nunc efficitur arcu elit, quis semper risus vestibulum eu. \n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Queue/README.md",
    "content": "## Queue Implementation\n\n### Queue using Linked List\n```python\nclass ListNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nclass Queue(object):\n    def __init__(self):\n        self.head = None\n        self.tail = None\n        self.size = 0\n\n    def __str__(self):\n        '''print Queue'''\n        res = ''\n        cur = self.head\n        while cur:\n            res += str(cur.val)\n            res += ' '\n            cur = cur.next\n        return res\n\n    def push(self, val):\n        node = ListNode(val)\n        if not self.head:\n            self.head = self.tail = node\n        else:\n            self.tail.next = node\n            self.tail = node\n        self.size += 1\n\n    def popleft(self):\n        if not self.head:\n            return None\n        pop_val = self.head.val\n        self.head = self.head.next\n        if self.head == None:\n            self.tail = None\n        self.size -= 1\n        return pop_val\n\n    def peekleft(self):\n        if self.head:\n            return self.head.val\n        return None\n\n    def get_size(self):\n        return self.size\n\n    def is_empty(self):\n        return self.size == 0\n```\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/README.md",
    "content": "## 二分查找\n\n![](/img/Algorithm/DataStructure/二分查找.gif)\n\n* 二分查找: [BinarySearch.py](/src/py3.x/DataStructure/BinarySearch.py)\n\n## 八大排序算法\n\n> Python 模版\n\n![](/img/Algorithm/DataStructure/Python/八大排序算法性能.png)\n\n| 名称 | 动图 | 代码 |\n| --- | --- | --- |\n| 冒泡排序 | ![](/img/Algorithm/DataStructure/冒泡排序.gif)    | [BubbleSort.py](/src/py3.x/DataStructure/BubbleSort.py)       |\n| 插入排序 | ![](/img/Algorithm/DataStructure/直接插入排序.gif) | [InsertSort.py](/src/py3.x/DataStructure/InsertionSort.py)    |\n| 选择排序 | ![](/img/Algorithm/DataStructure/简单选择排序.gif) | [SelectionSort.py](/src/py3.x/DataStructure/SelectionSort.py) |\n| 快速排序 | ![](/img/Algorithm/DataStructure/快速排序.gif)    | [QuickSort.py](/src/py3.x/DataStructure/QuickSort.py)         |\n| 希尔排序 | ![](/img/Algorithm/DataStructure/希尔排序.png)    | [ShellSort.py](/src/py3.x/DataStructure/ShellSort.py)         |\n| 归并排序 | ![](/img/Algorithm/DataStructure/归并排序.gif)    | [MergeSort.py](/src/py3.x/DataStructure/MergeSort.py)         |\n| 基数排序 | ![](/img/Algorithm/DataStructure/基数排序.gif)    | [RadixSort.py](/src/py3.x/DataStructure/RadixSort.py)         |\n\n补充: [JavaScript 模块](https://github.com/apachecn/Interview/tree/master/docs/Algorithm/DataStructure/JavaScript.md)\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Stack/README.md",
    "content": "## Stack Implementation\n\n### Stack using Linked List\n```python\nclass ListNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nclass Stack(object):\n    def __init__(self):\n        self.head = None\n        self.size = 0\n\n    def push(self, val):\n        new_node = ListNode(val)\n        new_node.next = self.head\n        self.head = new_node\n        self.size += 1\n\n    def pop(self):\n        if self.head == None:\n            return None\n        pop_val = self.head.val\n        self.head = self.head.next\n        self.size -= 1\n        return pop_val\n\n    def peek(self):\n        return self.head.val\n\n    def get_size(self):\n        return self.size\n\n    def is_empty(self):\n        return self.size == 0\n```\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/Binary Search.md",
    "content": "### Binary Search \n\n\n```python\ndef binarySearch(nums, target):\n    l, r = 0, len(nums) -1\n    while l <= r:\n        mid = l + ((r-l) >> 2)\n        if nums[mid] > target:\n            r = mid - 1\n        elif nums[mid] < target:\n            l = mid + 1\n\telse: \n\t    return mid\n    return -1\n```\n\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/DFS和BFS.md",
    "content": "# Graph Search，DFS, BFS\n\n\n\n## DFS/BFS \n\n可以让stack/queue记录更多一些的东西，因为反正stack/queue更像通用结构\n\n```\n     A\n   /    \\\n  C      B\n  \\     / \\\n   \\    D E\n    \\    /\n       F\n\n\ngraph = {'A': set(['B', 'C']),\n         'B': set(['A', 'D', 'E']),\n         'C': set(['A', 'F']),\n         'D': set(['B']),\n         'E': set(['B', 'F']),\n         'F': set(['C', 'E'])}\n```\n### DFS\n\n迭代版本\n```python\ndef dfs(graph, start): # iterative\n    visited, stack = [], [start]\n    while stack:\n        vertex = stack.pop()\n        if vertex not in visited:\n            visited.append(vertex)\n            stack.extend(graph[vertex] - set(visited))\n    return visited\nprint(dfs(graph, 'A')) # ['A', 'C', 'F', 'E', 'B', 'D'] 这只是其中一种答案 \n```\n\n递归版本\n```python\ndef dfs(graph, start, visited=None): # recursive\n    if visited is None:\n        visited = []\n    print('visiting', start)\n    visited.append(start)\n    for next in graph[start]:\n        if next not in visited:\n            dfs(graph, next, visited)\n    return visited\nprint(dfs(graph, 'A')) # ['A', 'C', 'F', 'E', 'B', 'D'] 这只是其中一种答案 \n```\n\n\n迭代打印出从出发点到终点的路径\n```python\ndef dfs_paths(graph, start, goal): # iterative\n    stack = [(start, [start])]\n    while stack:\n        (vertex, path) = stack.pop()\n        for next in graph[vertex] - set(path):\n            if next == goal:\n                yield path + [next]\n            else:\n                stack.append((next, path + [next]))\nprint(list(dfs_paths(graph, 'A', 'F'))) # [['A', 'C', 'F'], ['A', 'B', 'E', 'F']]\n```\n\n递归打印出从出发点到终点的路径\n```python\ndef dfs_paths(graph, start, goal, path=None): # recursive\n    if path is None:\n        path = [start]\n    if start == goal:\n        yield path\n    for next in graph[start] - set(path):\n        yield from dfs_paths(graph, next, goal, path + [next])\nprint(list(dfs_paths(graph, 'C', 'F'))) # [['C', 'A', 'B', 'E', 'F'], ['C', 'F']]\n```\n\n### BFS\n\n迭代版本，和DFS唯一的区别就是pop(0)而不是pop()\n```python\ndef bfs(graph, start): # iterative\n    visited, queue = [], [start]\n    while queue:\n        vertex = queue.pop(0)\n        if vertex not in visited:\n            visited.append(vertex)\n            queue.extend(graph[vertex] - set(visited))\n    return visited\nprint(bfs(graph, 'A')) # ['A', 'C', 'B', 'F', 'D', 'E']\n```\n\n\n返回两点之间的所有路径，第一个一定是最短的\n```python\ndef bfs_paths(graph, start, goal):\n    queue = [(start, [start])]\n    while queue:\n        (vertex, path) = queue.pop(0)\n        for next in graph[vertex] - set(path):\n            if next == goal:\n                yield path + [next]\n            else:\n                queue.append((next, path + [next]))\nprint(list(bfs_paths(graph, 'A', 'F'))) # [['A', 'C', 'F'], ['A', 'B', 'E', 'F']]\n```\n\n知道了这个特性，最短路径就很好搞了\n```python\ndef bfs_paths(graph, start, goal):\n    queue = [(start, [start])]\n    while queue:\n        (vertex, path) = queue.pop(0)\n        for next in graph[vertex] - set(path):\n            if next == goal:\n                yield path + [next]\n            else:\n                queue.append((next, path + [next]))\ndef shortest_path(graph, start, goal):\n    try:\n        return next(bfs_paths(graph, start, goal))\n    except StopIteration:\n        return None\nprint(shortest_path(graph, 'A', 'F'))  # ['A', 'C', 'F']\n```\n\n#### Improvement/Follow up\n\n1. 一旦BFS/DFS与更具体的，更有特性的data structure结合起来，比如binary search tree，那么BFS/DFS会针对这个tree traversal显得更有特性。\n2. it's worth mentioning that there is an optimized queue object in the collections module called [deque](https://docs.python.org/2/library/collections.html#collections.deque)) for which removing items from the beginning ( or popleft ) takes constant time as opposed to O(n) time for lists. \n\n\n\n### Resources\n\n1. [Depth-and Breadth-First Search](https://jeremykun.com/2013/01/22/depth-and-breadth-first-search/)\n2. [Edd Mann](http://eddmann.com/posts/depth-first-search-and-breadth-first-search-in-python/)\n3. [graph - Depth-first search in Python](https://codereview.stackexchange.com/questions/78577/depth-first-search-in-python)\n\n\n\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/Data Structure and Algorthim Review.md",
    "content": "\n\n### Data Structure and Algorthim Review\n\n\n\n- [x] Binary Search Tree\n\n      - [x] 插入\n\n            • 如果树为空，创建一个叶子节点，令该节点的key = k;\n            • 如果k小于根节点的key，将它插入到左子树中;\n            • 如果k大于根节点的key，将它插入到右子树中。\n\n      - [x] 遍历\n\n            • 前序:  根，左，右\n\n            • 中序：左，根，右 **有序**\n\n            • 后序：左，右，根\n\n      - [x] 搜索\n\n            - look up ： 是否存在\n\n              ​• 如果树为空，搜索失败;\n              ​• 如果根节点的key等于待搜索的值，搜索成功，返回根节点作为结果;\n              ​• 如果待搜索的值小于根节点的key，继续在左子树中递归搜索;\n              ​• 否则，待搜索的值大于根节点的key，继续在右子树中递归搜索。\t\n\n            - 最大元素和最小元素\n\n              ​\t• 最右和最左\n\n            - 前驱(Successor)和后继(predecessor)\n\n              ​\t给定元素x，它的后继元素y是满足y > x的最小值\n\n              ​\t\t• 如果x所在的节点有一个非空的右子树，则右子树中的最小值就是答案\n\n              ​\t\t• 否则我们需要向上回溯，找到最近的一个祖先，使得该祖先的左侧孩子，也为x的祖\t先。\n              ​\t\t\n\n      - [x] 删除\n\n               • 如果x没有子节点，或者只有一个孩子，直接将x“切下”;\n\n               • 否则，x有两个孩子，我们用其右子树中的最小值替换掉x，然后将右子树中的这一最小值“切掉”。\t\n\n\n\n\n\n\n- [x] 递归\n      - [x] 入门\n\n            - 回文\n            - 阶乘 factorial， 慕指数\n            - 分形\n            - Tower of Hanoi\n\n      - [x] 排列 Permutation\n\n      - [x] 子集 Subsets\n\n      - [ ] backtracking\n\n\n- [x] dynamic programming    \n\n      -  coin change\n\n      -  longest common subsequence\n\n      -  edit distance\n\n         ​\n\n\n\n\n-[ ] majority element\n\n\n\n- [ ] 随机\n      - 水塘抽样\n      - 洗牌\n\n\n-[ ] 荷兰旗问题\n\n\n-[ ] quick select\n\n\n-[ ] median of two sorted array\n-[ ] regular expression\n\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/Dynamic Programming.md",
    "content": "### Dynamic Programming\n\n\n\n- Fibonacci Numbers\n- Shortest Path (no cycles)\n\n\n\n\n\n\n\n- subproblems\n\n- guessing\n\n- relate subproblems\n\n- recurse & memoize (bulid DP table)\n\n- solve original problem\n\n  ​\n\n\n\n\n\n\n​\t\t\t\n​\t\t\n​\t\n\n感觉DP有几类：\n\n- 容易写出递推公式的\n\n- 画表更容易理解的\n\n  ​\n\n*    DP ≈ “controlled brute force”\n\n*    DP ≈ recursion + re-use\n\n     ​\t\t\n\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/Java各种类型的转换.md",
    "content": "之前在写java程序的时候，经常会遇到很多的需要需要转换基础数据类型的情况，然后我就一直去记录这些情况，今天做了一下总结，当然转换的方法肯定不止我写的这些，有的我可能只会写其中的一种，以后再遇到其他的情况的话，我会慢慢来补充，希望这篇文章会对大家能有所帮助。\n\n------\n\n# String的转换\n\n首先介绍一下String类型的转换，一般遇到的情况可能会有以下几种：Strng转int，String转long，String转byte数组，String转float，下面主要介绍这四种情况。\n\n## String转int\n\n把String类型转换为int类型，常用的有以下三种方法：\n\n```\npublic class StringToInt {\n\tpublic static void main(String[] args) {\n\t\tString number = \"123456\";\n\t\tint num1 = Integer.parseInt(number);//使用Integer的parseInt方法\n\t\tint num2 = new Integer(number);//强制转换\n\t\tint num3 = Integer.valueOf(number).intValue();//先转Integer类型，再调用intValue()转为int\n\t}\n}\n```\n\n## String转long\n\n把String类型转换为long类型的方法跟上面的方法类似。\n\n```\npublic class StringToLong {\n\tpublic static void main(String[] args) {\n\t\tString number = \"1234567890\";\n\t\tlong num1 = Long.parseLong(number);//调用Long类型的parseLong方法\n\t\tlong num2 = new Long(number);//强制转换\n\t\tlong num3 = Long.valueOf(number).longValue();//先转换Long类型，再使用longValue方法转为long\n\t}\n}\n```\n\n## String转float\n\n把String类型转换为float类型的方法也跟上面的类似。\n\n```\npublic class StringToFloat {\n\tpublic static void main(String[] args) {\n\t\tString number = \"1234.202\";\n\t\tfloat num1 = Float.parseFloat(number);//调用Float的parseFloat方法\n\t\tfloat num2 = new Float(number);//强制转换\n\t\tfloat num3 = Float.valueOf(number).floatValue();//先转为Float类型再使用floatValue转为float\n\t}\n}\n```\n\n## String转byte[]\n\nString类型转byte数组方法一般使用String类自带的`getBytes()`方法。\n\n```\npublic class StringToByte {\n\tpublic static void main(String[] args) {\n\t\tbyte[] num = new byte[200];\n\t\tString number = \"1234567890\";\n\t\tnum = number.getBytes();\n\t}\n}\n```\n\n这里补充一个path类型转换为String类型的方法：\n\n```\nString fileName=path.getFileName().toString();\n```\n\n------\n\n# long类型转换\n\nlong类型的转换，这一部分用的情况也很多，下面介绍几种常见的情况。\n\n## long转String\n\nlong类型转String类型，这里主要介绍三种方法：\n\n```\npublic class LongToString {\n\tpublic static void main(String[] args) {\n\t\tlong number = 1234567890l;\n\t\tString num1 = Long.toString(number);//Long的tostring方法\n\t\tString num2 = String.valueOf(number);//使用String的valueOf方法\n\t\tString num3 = \"\" + number;//这个应该属于强制转换吧\n\t}\n}\n```\n\n## long转int\n\nlong类型转换为int类型，这里也主要介绍三种方法：\n\n```\npublic class LongToInt {\n\tpublic static void main(String[] args) {\n\t\tlong number = 121121121l;\n\t\tint num1 = (int) number;// 强制类型转换\n\t\tint num2 = new Long(number).intValue();// 调用intValue方法\n\t\tint num3 = Integer.parseInt(String.valueOf(number));// 先把long转换位字符串String，然后转换为Integer\n\t}\n}\n```\n\n## long与byte数组的相互转换\n\n一直都感觉byte数组转换比较繁琐，这里也不再叙述，我就给出一篇别人的博客让大家作为参考吧，这里面byte数组与多种数据类型的转换——[ java Byte和各数据类型(short,int,long,float,double)之间的转换](http://blog.csdn.net/cshichao/article/details/9813973)\n\n------\n\n# int类型的转换\n\nint类型的转换也是我们经常使用的情况，下面也主要介绍几种常见的情况。\n\n## int转String\n\nint类型转换为String类型与long转String的类似，一般也有以下三种方法。\n\n```\npublic class IntToString {\n\tpublic static void main(String[] args) {\n\t\tint number = 121121;\n\t\tString num1 = Integer.toString(number);//使用Integer的toString方法\n\t\tString num2 = String.valueOf(number);//使用String的valueOf方法\n\t\tString num3 = \"\" + number;//也是强制转换吧\n\t}\n}\n```\n\n## int与Byte的相互转换\n\n关于int类型与byte[]数组的转换，一般情况下，我们使用条件都是在这里转换过来，在另外一个地方就要转换回来，这里介绍两种int与byte数组互相转换的方式。\n\n```\n//int类型转换为byte[]数组\npublic static byte[] intToByteArray(int i) {\n\tbyte[] result = new byte[4];\n\t// 由高位到低位\n\tresult[0] = (byte) ((i >> 24) & 0xFF);\n\tresult[1] = (byte) ((i >> 16) & 0xFF);\n\tresult[2] = (byte) ((i >> 8) & 0xFF);\n\tresult[3] = (byte) (i & 0xFF);\n\treturn result;\n}\n\n//byte数组转换为int类型\npublic static int byteArrayToInt(byte[] bytes) {\n\tint value = 0;\n\t// 由高位到低位\n\tfor (int i = 0; i < 4; i++) {\n\t\tint shift = (4 - 1 - i) * 8;\n\t\tvalue += (bytes[i] & 0x000000FF) << shift;// 往高位游\n\t}\n\treturn value;\n}\n```\n\n还有一种为：\n\n```\n//int类型转换为byte[]数组\npublic static byte[] intToByteArray(int x) {\n\tbyte[] bb = new byte[4];\n\tbb[3] = (byte) (x >> 24);\n\tbb[2] = (byte) (x >> 16);\n\tbb[1] = (byte) (x >> 8);\n\tbb[0] = (byte) (x >> 0);\n\treturn bb;\n}\n\n//byte数组转换为int类型\npublic static int byteArrayToInt(byte[] bb) {\n\treturn (int) ((((bb[3] & 0xff) << 24) | ((bb[2] & 0xff) << 16) | ((bb[1] & 0xff) << 8) | ((bb[0] & 0xff) << 0)));\n}\n```\n\n## int转long\n\nint类型转换为long类型的情况并不是大多，这里主要接收几种转换方法：\n\n```\npublic class IntToLong {\n\tpublic static void main(String[] args) {\n\t\tint number = 123111;\n\t\tlong num1 = (long) number;//强制\n\t\tlong num2 = Long.parseLong(new Integer(number).toString());//先转String再进行转换\n\t\tlong num3 = Long.valueOf(number);\n\t}\n}\n```\n\n## int转Interger\n\nint类型转换为Interger类型的情况，我是基本上每怎么遇到过，在这里也上网查询一些资料找到了两种方法。\n\n```\npublic class IntToInterge {\n\tpublic static void main(String[] args) {\n\t\tint number = 123456;\n\t\tInteger num1 = Integer.valueOf(number);\n\t\tInteger num2 = new Integer(number);\n\t}\n}\n```\n\n------\n\n# byte数组的转换\n\n关于byte数组的转换，上面有几个都是它们只见相互转换的，所以这里就不再介绍那么多，只介绍一个byte数组转换String类型的方法，其他的类型可以通过String类型再进行转换。\n\nbyte数组转String类型的方法经常用的可能就是下面这种方法。\n\n```\npublic class ByteToString {\n\tpublic static void main(String[] args) {\n\t\tbyte[] number = \"121121\".getBytes();\n\t\tString num1 = new String(number);\n\t}\n}\n```\n\n------\n\n最后简单补充以下Java基本数据类型的一些知识：\n\n| 类型     | 字节数  | 类名称      | 范围                                       |\n| ------ | ---- | -------- | ---------------------------------------- |\n| int    | 4字节  | Interger | -2147483648 ~ 2147483647                 |\n| short  | 2字节  | Short    | -32768 ～ 32767                           |\n| long   | 8字节  | Long     | -9223372036854775808 ～ 9223372036854775807 |\n| byte   | 1字节  | Byte     | -128 ～ 127                               |\n| float  | 4字节  | Float    |                                          |\n| double | 8字节  | Double   |                                          |"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/LinkedList技巧.md",
    "content": "# LinkedList\n\n结点定义如下：\n\n\tclass ListNode(object):\n\t    def __init__(self, x):\n\t        self.val = x\n\t        self.next = None\n\n\n可以使用的技巧包括:\n\n\n## Dummy head\n\n有的时候因为边界条件，需要判定是否是list的head，因为处理起来会有些不同，而创造一个dummy head则可以极大的解决一些问题。\n\n```\n\tdummy = ListNode(-1)\n        dummy.next = head\n```\n\n## 双指针\n\n- 19. Remove Nth Node From End of List\n\n两个指针p,q， q先走n步，然后p和q一起走，直到q走到结点，删除p.next解决。\n\n理解： 先走了n步，q始终在p前方n个，这样q走到末尾，p的下一个则是距离尾端n个的，画个图还是容易理解。\n\n\n- 160. Intersection of Two Linked Lists\n\n如果两个linkedlist有intersection的话，可以看到，其实如果一开始我们就走到b2的话，那么我们就可以两个pointer一个一个的对比，到哪一个地址一样，接下来就是intersection部分。\n\n就一开始把长的那条list走掉多余部分。\n还有这里保证了是无环的状况\n\n\n\n```\nA:          a1 → a2\n                   ↘\n                     c1 → c2 → c3\n                   ↗            \nB:     b1 → b2 → b3\n```\n\n\n## 快慢指针\n\n- 141. Linked List Cycle\n\n用两个指针，一个每次走两步，一个每次走一步，如果慢的最终和快的相遇，那么说明有环，否则没有环，直观的理解是如果两个跑的速度不一的人进操场跑步，那么最终慢的会追上快的.\n\n\n\n## Reverse Linked List\n\n- 206. Reverse Linked List\n\nloop版本用prev, cur ,nxt 三个指针过一遍，recursion版本如下，可以再消化消化\n\n```\nclass Solution(object):\n    def reverseList(self, head):\n        \"\"\"\n        :type head: ListNode\n        :rtype: ListNode\n        \"\"\"\n        return self.doReverse(head, None)\n        \n        \n    def doReverse(self, head, newHead):\n        if head == None:\n            return newHead\n        nxt = head.next\n        head.next = newHead\n        return self.doReverse(nxt, head)\n```\n\n\n## 寻找LinkedList中间项\n\n依旧使用双指针，快慢指针：快指针每次走两步，慢指针每次走一步，快指针如果到头了，那么慢指针也会在中间了，这个中间可以考量，如果是奇数的话必然是中间。\n\n如果是偶数则是偏前面的中间一项\n\n```\n1 -> 2 -> 3 -> 4: 2 \n1 -> 2 -> 3 -> 4 -> 5 -> 6 : 3\n1 -> 2 -> 3 -> 4 -> 5 -> 6 -> 7 -> 8 : 4\n\n```\n\n算法：\n\n```\n\ndef findMid(head):\n    if head == None or head.next == None:\n        return head\n\n    slow = head\n    fast = head\n\n    while fast.next and fast.next.next:\n        slow = slow.next\n        fast = fast.next.next\n        \n    return slow\n```\n\n\n\n\n\n\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/Python刷题技巧笔记.py",
    "content": "from __future__ import print_function\n\nimport heapq\n\n# python有无数奇技淫巧和许多人不知道的秘密，这里用简洁的语言一条条表述出来，不断更新, 大家一起贡献！\n\n# 1. python 排序\n# 用lst.sort() 而不是nlst = sorted(lst)， 区别在于lst.sort()是 in-place sort，改变lst, sorted会创建新list，成本比较高。\n\n# 2. xrange和range的区别\n# range会产生list存在memory中，xrange更像是生成器，generate on demand所以有的时候xrange会更快\n\n# 3. python处理矩阵\nrow = len(matrix)\ncol = len(matrix[0]) if row else 0\n# 这样写通用的原因是， 当matrix = [], row = 0, col = 0\n\n# 4. python列表生成式\nlst = [0 for i in range(3)] # lst = [0,0,0]\nlst = [[0 for i in range(3)] for j in range(2)]  # lst =  [[0, 0, 0], [0, 0, 0]]\n# 下面这种写法危险：\n# lst1 = [ 0, 0, 0 ]\n# lst2  = [lst1] * 2  # lst2 = [ [0,0,0] , [0,0,0] ]\n# lst2[0][0]  = 1  # lst2 = [ [1,0,0], [1,0,0]]\n# 因为lst1是object，改一个相当于全改, 这样写会踩坑\n\n# 5. D.get(key, default)\n# 如果这个key 没有在dict里面，给它一个默认值：\nD = {}\nif 1 in D:\n  val = D[1]\nelse :\n  val = 0\n# 等同于这样写：\nval = D.get(1, 0)\n\n# 6. 字典赋值\nif key in D:\n  D[key].append(1)\nelse :\n  D[key] = []\n\n# 7. 字符串反转\n# python字符串没有reverse函数，只能str[::-1]\nstring[::-1]\n# python的list可以直接reverse()，因此也可以借用这个特性\n\"\".join([string].reverse())\n\n# 8. 快速统计\nimport collections\nlst = [1, 1, 1, 2, 3, 4, 5, 5]\ncollections.Counter(lst) # Counter({1: 3, 5: 2, 2: 1, 3: 1, 4: 1})\n\n# 9. python自带小顶堆heapq\n# Python有built-in heap, 默认min heap.\nheapq.heappush(heap, item) # 把item添加到heap中（heap是一个列表）\n\nheapq.heappop(heap) # 把堆顶元素弹出，返回的就是堆顶\n\nheapq.heappushpop(heap, item) # 先把item加入到堆中，然后再pop，比heappush()再heappop()要快得多\n\nheapq.heapreplace(heap, item) # 先pop，然后再把item加入到堆中，比heappop()再heappush()要快得多\n\nheapq.heapify(x) # 将列表x进行堆调整，默认的是小顶堆\n\nheapq.merge(*iterables) # 将多个列表合并，并进行堆调整，返回的是合并后的列表的迭代器\n\nheapq.nlargest(n, iterable, key=None) # 返回最大的n个元素（Top-K问题）\n\nheapq.nsmallest(n, iterable, key=None) # 返回最小的n个元素（Top-K问题）\n\n# 如何来用它实现max heap呢，看到过一个有意思的方法是把key取负，比如把100变成-100，5变成-5\n\nimport heapq\nmylist = [1, 2, 3, 4, 5, 10, 9, 8, 7, 6]\nlargest = heapq.nlargest(3, mylist) # [10, 9, 8]\nsmallest = heapq.nsmallest(3, mylist) # [1, 2, 3]\n\n# 10. 双端队列deque [http://stackoverflow.com/questions/4098179/anyone-know-this-python-data-structure]\n# 可以很简单的.popleft(), .popright(), .appendleft(), .appendright(),最关键的是时间是O(1), 而用list来模拟队列是O(n)的时间复杂度\n# 还有很好用的rotate函数，\n# 一个简单的跑马灯程序\nimport sys\nimport time\nfrom collections import deque\n\nfancy_loading = deque('>--------------------')\n\nwhile True:\n    print('\\r%s' % ''.join(fancy_loading))\n    fancy_loading.rotate(1)\n    sys.stdout.flush()\n    time.sleep(0.08)\n\n# 11. 用yield 不用return，可以返回一个generator\n\n# 12. 符号～的巧妙应用\nfor i in range(n):\n  # 这里的```[~i]``` 意思就是 ```[n-1-i]```\n  a[~i] = 1\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/Recusrion & BackTracking.md",
    "content": "#Recusrion & BackTracking\n\n##Recusrion\n\n### DrawFractal\n\n```\nvoid DrawFractal(double x, double y, double w, double h)\n{\n  DrawTriangel(x, y, w, h);\n  if(w < .2 || h < .2) return ;\n  double halfH = h/2;\n  double halfw = w/2;\n  DrawFractal(x, y, halfW, halfH);\n  DrawFractal(x + halfW/2, y + halfH, halfW, halfH);\n  DrawFractal(x + halfW, y, halfW, halfH);\n}\n```\n\n\nSierpinski triangle更伪码的写法：\n\n```\nvoid DrawFractal (x, y, w, h){\n\tif (too small) return ;\n\tDrawTriangle(x, y, w, h);\n\tDrawFractal(.left);\n\tDrawFractal(.top);\n\tDrawFractal(.right);\n}\n```\n\n实际上老师故意调了里面几句代码的顺序，让来看到虽然结果相同，但是画的过程是不一样的。\n\n然后老师还在黑板上画了过程树，分枝是怎样的，实际上当学到DFS的preOrder， inOrder 和 postOrder的时候会更印象深刻。\n\n一个分支走完之后再回去走另一些。\n\n\n### DrawMondrian\n\n\n \n```\nvoid DrawMondrian(double x, double y, double w, double h){\n  \n  if(w < 1 || h < 1) return ;// base case\n  \n  FillRectangle(x,y,w,h,RandomColor()); // fill background\n\n  switch(RandomInteger(0, 2)){\n    case 0:  // do nothing\n      break; \n    case 1:  // bisect vertically\n      double midX = RandomReal(0,w);\n      DrawBlackLine( x + midX, y, h);\n      DrawMondrian(x, y, midX, h);\n      DrawMondrian(x + midx, y, w- midX, h);\n      break;\n    case 2:  // bisect horizontally\n      double midY = RandomReal(0,h);\n      DrawBlackLine( x, y+ midY, h);\n      DrawMondrian(x, y, w, midY);\n      DrawMondrian(x, y+midY,w, midY);\n      break;\n      }\n}\n```\n\n\n### The tower of Hanoi\n\n\n```\nvoid MoveTower(int n, char src, char dst, char tmp){\n  if (n > 0){\n    MoveTower(n - 1, src, tmp, dst );\n    MoveSingleDisk(src, dst);\n    MoveTower(n -1, tmp, dst, src);\n  }\n}\n\n```\n\n\n### Permutation\n\n老师说permutation 和 subset 是 mother problems of all recursion.\n\n\ngiven a string， print out its all permutations\n\n思路如下：\n\n- 使用了的string sofar，以及还未使用的string rest\n- 一开始rest就是给的string本身，然后sofar是空\n- 每次挑一个rest里面的char，然后递归的再把rest剩下的拿来permutation,这样每次都会有一个char从rest shuffle到sofar\n- n 次之后 rest为空，制造了一个permutation \n\n\n```\nvoid RecPermute(string sofar, string rest){\n  if(rest = \"\"){\n    cout << soFar << endl;\n  } else {\n     for(int i = 0 ; i < rest.length(); i++){\n     string next = soFar + rest[i];\n     string remaining = rest.substr(0,i) + rest.substr(i+1);\n     RecPermute(next, remaining);\n    }\n  }\n}\n\n\n// \"wrapper\" function\nvoid ListPermutations(string s)\n{\n  RecPermute(\"\",s);\n}\n```\n\n\n老师的黑板图真的是击中要害。\n\n因为老师强调的是，也要用mind来trace它是如何操作的。\n\n\n\n### Subsets \n\n\n```\nvoid RecSubsets(string soFar, string rest)\n{\n  if(rest = \"\")\n    cout << soFar << endl;\n  else {\n    // add to subset, remove from rest, recur\n    RecSubsets(soFar + rest[0],rest.substr(1));\n    //don't add to substr, remove from rest, recur\n    RecSubsets(soFar, rest.substr(1));\n  }\n}\n\n\nvoid ListSubsets(string str)\n{\n  RecSubsets(\"\",str);\n}\n\n```\n\n代码非常容易理解\n\n\n比较一下：两个都是有关选择，permutation是每次选哪一个char，而subsets是选择这个char 是否in.\n\n两个recursion tree都是有branching 和 depth的， depth都是n，每次选一个，知道n个选完.\n\nbranching是how many recusive calls 每次made，subset每次都是两个，in/out，而permutation则是n，n-1.......grows very quickly.\n\n因为permutation是n！，subsets是2^n，跟树对应。这些都是比较intractable的问题，并不是因为recursion，而是问题本身的复杂度。\n\n\n这两个问题都是exhaustive的，然而，我们会更多碰到一些问题，有着\n\nsimilar exhaustive structure，但是遇到'satisfactory' outcome就会stop的 -> 也就是backtracking了.\n\n##BackTracking\n\n\n\n### pseudocode\n\n把问题转成decision problem，然后开始make choice.\n\n```\nbool Solve(configuration conf)\n{\n  if (no more choices) // BASE CASE\n    return (conf is goal state);\n\n  for (all available choices){\n    try one choice c;\n    // sove from here, it works out. you're done.\n    if (Solve(conf with choice c made))  return true;\n    unmake choice c;\n  } \n  return false;  //tried all choices, no soln found\n}\n```\n\n\n###IsAnagram\n\n\n```\nbool IsAnagram(string soFar, string rest, Lexicon & lex)\n{\n  if(rest == \"\"){\n      if(lex.contains(soFar)){\n          cout << soFar << endl;\n          return true;\n      }\n  } else {\n      for(int i = 0; i < rest.length() ; i++ ){\n          string next = soFar + rest[i];\n          string remaining = rest.substr(0,i) + rest.substr(i+1);\n          if(IsAnagram(next, remaining, lex)) return true;\n      }\n  }\n  return false;\n}\n```\n\n\n### 8 Queens \n\n\n```\n\nbool Solve(Grid<bool> &board, int col)\n{\n  if(col > = board.numCols()) return true;\n  \n  for(int rowToTry = 0; rowToTry < board.numRows(); rowToTry++){\n   if (IsSafe(board,rowToTry, col)){\n        PlaceQueen(board,rowToTry,col);\n\tif (Solve(board,col+1)) return true;\n\tRemoveQueen(board,rowToTry, col);\n     }\n  }\n  return false;\n}\n\n```\n\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/backtracking思路.md",
    "content": "## backtracking 全集\n\n### 回溯是啥\n用爬山来比喻回溯，好比从山脚下找一条爬上山顶的路,起初有好几条道可走,当选择一条道走到某处时,又有几条岔道可供选择,只能选择其中一条道往前走,若能这样子顺利爬上山顶则罢了,否则走到一条绝路上时,只好返回到最近的一个路口,重新选择另一条没走过的道往前走。如果该路口的所有路都走不通,只得从该路口继续回返。照此规则走下去,要么找到一条到达山顶的路,要么最终试过所有可能的道,无法到达山顶。\n回溯是一种穷举，但与brute force有一些区别，回溯带了两点脑子的，并不多，brute force一点也没带。\n第一点脑子是回溯知道回头；相反如果是brute force,发现走不通立刻跳下山摔死，换第二条命从头换一条路走。\n第二点脑子是回溯知道剪枝；如果有一条岔路上放了一坨屎，那这条路我们不走，就可以少走很多不必要走的路。\n\n还有一些爱混淆的概念：递归，回溯，DFS。\n回溯是一种找路方法，搜索的时候走不通就回头换路接着走，直到走通了或者发现此山根本不通。\nDFS是一种开路策略，就是一条道先走到头，再往回走一步换一条路走到头，这也是回溯用到的策略。在树和图上回溯时人们叫它DFS。\n递归是一种行为，回溯和递归如出一辙，都是一言不合就回到来时的路，所以一般回溯用递归实现；当然也可以不用，用栈。\n以下以回溯统称，因为这个词听上去很文雅。\n\n### 识别回溯\n判断回溯很简单，拿到一个问题，你感觉如果不穷举一下就没法知道答案，那就可以开始回溯了。\n一般回溯的问题有三种：\n\n1. Find a path to success 有没有解\n2. Find all paths to success 求所有解\n\t- 2.1 求所有解的个数，\n\t- 2.2 求所有解的具体信息\n3. Find the best path to success 求最优解\n\n理解回溯：给一堆选择, 必须从里面选一个. 选完之后我又有了新的一组选择. ```This procedure is repeated over and over until you reach a final state. If you made a good sequence of choices, your final state is a goal state; if you didn't, it isn't.```\n\n回溯可以抽象为一棵树，我们的目标可以是找这个树有没有good leaf，也可以是问有多少个good leaf，也可以是找这些good leaf都在哪，也可以问哪个good leaf最好，分别对应上面所说回溯的问题分类。\ngood leaf都在leaf上。good leaf是我们的goal state，leaf node是final state，是解空间的边界。\n\n对于第一类问题(问有没有解)，基本都是长着个样子的，理解了它，其他类别迎刃而解：\n```java\nboolean solve(Node n) {\n    if n is a leaf node {\n        if the leaf is a goal node, return true\n        else return false\n    } else {\n        for each child c of n {\n            if solve(c) succeeds, return true\n        }\n        return false\n    }\n}\n```\n请读以下这段话以加深理解：\n```Notice that the algorithm is expressed as a boolean function. This is essential to understanding the algorithm. If solve(n) is true, that means node n is part of a solution--that is, node n is one of the nodes on a path from the root to some goal node. We say that n is solvable. If solve(n) is false, then there is no path that includes n to any goal node.```\n\n还不懂的话请通读全文吧：[Backtracking - David Matuszek](https://www.cis.upenn.edu/~matuszek/cit594-2012/Pages/backtracking.html)\n\n关于回溯的三种问题，模板略有不同，\n第一种，返回值是true/false。\n第二种，求个数，设全局counter，返回值是void；求所有解信息，设result，返回值void。\n第三种，设个全局变量best，返回值是void。\n\n第一种：\n```java\nboolean solve(Node n) {\n    if n is a leaf node {\n        if the leaf is a goal node, return true\n        else return false\n    } else {\n        for each child c of n {\n            if solve(c) succeeds, return true\n        }\n        return false\n    }\n}\n```\n第二种：\n```java\nvoid solve(Node n) {\n    if n is a leaf node {\n        if the leaf is a goal node, count++, return;\n        else return\n    } else {\n        for each child c of n {\n            solve(c)\n        }\n    }\n}\n```\n第三种：\n```java\nvoid solve(Node n) {\n    if n is a leaf node {\n        if the leaf is a goal node, update best result, return;\n        else return\n    } else {\n        for each child c of n {\n            solve(c)\n        }\n    }\n}\n```\n题目\n\n八皇后 N-Queens\n\n问题\n\n1. 给个n，问有没有解；\n2. 给个n，有几种解；(Leetcode N-Queens II)\n3. 给个n，给出所有解；(Leetcode N-Queens I)\n\n解答\n\n1.有没有解\n\n怎么做：一行一行的放queen，每行尝试n个可能，有一个可达，返回true；都不可达，返回false.\n\n边界条件leaf:放完第n行 或者 该放第n+1行(出界，返回)\n\n目标条件goal:n行放满且isValid，即目标一定在leaf上\n\nhelper函数：\nboolean solve(int i, int[][] matrix)\n在进来的一瞬间，满足property：第i行还没有被放置，前i-1行放置完毕且valid\nsolve要在给定的matrix上试图给第i行每个位置放queen。\n```java\npublic static boolean solve1(int i, List<Integer> matrix, int n) {\n    if (i == n) {\n        if (isValid(matrix))\n            return true;\n        return false;\n    } else {\n        for (int j = 0; j < n; j++) {\n            matrix.add(j);\n            if (isValid(matrix)) {    //剪枝\n                if (solve1(i + 1, matrix, n)) \n                    return true;\n            }\n            matrix.remove(matrix.size() - 1);\n        }\n        return false;\n    }\n}\n```\n2.求解的个数\n\n怎么做：一行一行的放queen，每行尝试n个可能。这回因为要找所有，返回值就没有了意义，用void即可。在搜索时，如果有一个可达，仍要继续尝试；每个子选项都试完了，返回.\n\n边界条件leaf:放完第n行 或者 该放第n+1行(出界，返回)\n\n目标条件goal:n行放满且isValid，即目标一定在leaf上\n\nhelper函数：\nvoid solve(int i, int[][] matrix)\n在进来的一瞬间，满足property：第i行还没有被放置，前i-1行放置完毕且valid\nsolve要在给定的matrix上试图给第i行每个位置放queen。\n这里为了记录解的个数，设置一个全局变量(static)int是比较efficient的做法。\n```java\npublic static void solve2(int i, List<Integer> matrix, int n) {\n    if (i == n) {\n        if (isValid(matrix))\n            count++;\n        return;\n    } else {\n        for (int j = 0; j < n; j++) {\n            matrix.add(j);\n            if (isValid(matrix)) {    //剪枝\n                solve2(i + 1, matrix, n); \n            }\n            matrix.remove(matrix.size() - 1);\n        }\n    }\n}\n```\n3.求所有解的具体信息\n\n怎么做：一行一行的放queen，每行尝试n个可能。返回值同样用void即可。在搜索时，如果有一个可达，仍要继续尝试；每个子选项都试完了，返回.\n\n边界条件leaf:放完第n行 或者 该放第n+1行(出界，返回)\n\n目标条件goal:n行放满且isValid，即目标一定在leaf上\n\nhelper函数：\nvoid solve(int i, int[][] matrix)\n在进来的一瞬间，满足property：第i行还没有被放置，前i-1行放置完毕且valid\nsolve要在给定的matrix上试图给第i行每个位置放queen。\n这里为了记录解的具体情况，设置一个全局变量(static)集合是比较efficient的做法。\n当然也可以把结果集合作为参数传来传去。\n```java\npublic static void solve3(int i, List<Integer> matrix, int n) {\n    if (i == n) {\n        if (isValid(matrix))\n            result.add(new ArrayList<Integer>(matrix));\n        return;\n    } else {\n        for (int j = 0; j < n; j++) {\n            matrix.add(j);\n            if (isValid(matrix)) {    //剪枝\n                solve3(i + 1, matrix, n); \n            }\n            matrix.remove(matrix.size() - 1);\n        }\n    }\n}\n```\n优化\n\n上面的例子用了省空间的方法。\n由于每行只能放一个，一共n行的话，用一个大小为n的数组，数组的第i个元素表示第i行放在了第几列上。\n\nUtility(给一个list判断他的最后一行是否和前面冲突):\n```java\npublic static boolean isValid(List<Integer> list){\n    int row = list.size() - 1;\n    int col = list.get(row);\n    for (int i = 0; i <= row - 1; i++) {\n        int row1 = i;\n        int col1 = list.get(i);\n        if (col == col1)\n            return false;\n        if (row1 - row == col1 - col)\n            return false;\n        if (row1 - row == col - col1)\n            return false;\n    }\n    return true;\n    \n}\n```\n\n参考[Backtracking回溯法(又称DFS,递归)全解](https://segmentfault.com/a/1190000006121957) \n以及 [Python Patterns - Implementing Graphs](https://www.python.org/doc/essays/graphs/)\n\n\n\n## 以Generate Parentheses为例，backtrack的题到底该怎么去思考？\n\n\n所谓Backtracking都是这样的思路：在当前局面下，你有若干种选择。那么尝试每一种选择。如果已经发现某种选择肯定不行（因为违反了某些限定条件），就返回；如果某种选择试到最后发现是正确解，就将其加入解集\n\n所以你思考递归题时，只要明确三点就行：选择 (Options)，限制 (Restraints)，结束条件 (Termination)。即“ORT原则”（这个是我自己编的）\n\n\n\n\n对于这道题，在任何时刻，你都有两种选择：\n1. 加左括号。\n2. 加右括号。\n\n同时有以下限制：\n1. 如果左括号已经用完了，则不能再加左括号了。\n2. 如果已经出现的右括号和左括号一样多，则不能再加右括号了。因为那样的话新加入的右括号一定无法匹配。\n\n结束条件是：\n左右括号都已经用完。\n\n结束后的正确性：\n左右括号用完以后，一定是正确解。因为1. 左右括号一样多，2. 每个右括号都一定有与之配对的左括号。因此一旦结束就可以加入解集（有时也可能出现结束以后不一定是正确解的情况，这时要多一步判断）。\n\n递归函数传入参数：\n限制和结束条件中有“用完”和“一样多”字样，因此你需要知道左右括号的数目。\n当然你还需要知道当前局面sublist和解集res。\n\n因此，把上面的思路拼起来就是代码：\n\n\tif (左右括号都已用完) {\n\t  加入解集，返回\n\t}\n\t//否则开始试各种选择\n\tif (还有左括号可以用) {\n\t  加一个左括号，继续递归\n\t}\n\tif (右括号小于左括号) {\n\t  加一个右括号，继续递归\n\t}\n\t\n\t\n\t\n你帖的那段代码逻辑中加了一条限制：“3. 是否还有右括号剩余。如有才加右括号”。这是合理的。不过对于这道题，如果满足限制1、2时，3一定自动满足，所以可以不判断3。\n\n这题其实是最好的backtracking初学练习之一，因为ORT三者都非常简单明显。你不妨按上述思路再梳理一遍，还有问题的话再说。\n\n\n\n以上文字来自 1point3arces的牛人解答\n\n\n\nBacktracking 伪码\n\n\n```\nPick a starting point.\nwhile(Problem is not solved)\n\tFor each path from the starting point.\n\t\tcheck if selected path is safe, if yes select it\n                and make recursive call to rest of the problem\n\t\tIf recursive calls returns true, then return true.\n\t\telse undo the current move and return false.\n\tEnd For\n\tIf none of the move works out, return false, NO SOLUTON.\n\n```\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/delete_node_in_a_linked_list问题.md",
    "content": "##Delete Node in a Linked List问题\n\n\nThis is a LeetCode question, I knew its solution, but wondering about why my code not work.\n\n\n>Write a function to delete a node (except the tail) in a singly linked list, given only access to that node.\n\n>Supposed the linked list is 1 -> 2 -> 3 -> 4 and you are given the third node with value 3, the linked list should become 1 -> 2 -> 4 after calling your function\n\n\nAt first glance, my intution is delete like an array:\n\nshift all the node values one front, then delete the tail, here's my implementation and test case:\n\n    \n\t\n\tclass ListNode(object):\n\t    def __init__(self, x):\n\t        self.val = x\n\t        self.next = None\n\t        \n\tnode1 = ListNode(1)\n\tnode2 = ListNode(2)\n\tnode3 = ListNode(3)\n\tnode4 = ListNode(4)\n\tnode5 = ListNode(5)\n\t\n\tnode1.next = node2\n\tnode2.next = node3\n\tnode3.next = node4\n\tnode4.next = node5\n\t\n\t\n\t    \n\tdef deleteNode(node):\n\t    \"\"\"\n\t    :type node: ListNode\n\t    :rtype: void Do not return anything, modify node in-place instead.\n\t    \"\"\"\n\t    while node.next:\n\t        node.val = node.next.val\n\t        node = node.next\n\t    node = None\n\t    \n\t    \n\tdeleteNode(node4)\n\t\nBut After deletion, it has two 5 value nodes, the tail was still kept, can anyone please explain to me what's wrong here?\n\n\tdeleteNode(node4)\n\t\n\tnode1.val\n\tOut[162]: 1\n\t\n\tnode1.next.val\n\tOut[163]: 2\n\t\n\tnode1.next.next.val\n\tOut[164]: 3\n\t\n\tnode1.next.next.next.val\n\tOut[165]: 5\n\t\n\tnode1.next.next.next.next.val\n\tOut[166]: 5\n\n\nReally appreciate any help."
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/python_base.py",
    "content": "# _*_ coding: utf-8 _*_\n\n\"\"\"类型和运算----类型和运算----类型和运算----类型和运算----类型和运算----类型和运算----类型和运算----类型和运算----类型和运算----类型和运算----类型和运算\"\"\"\n\n#-- 寻求帮助:\n    dir(obj)            # 简单的列出对象obj所包含的方法名称，返回一个字符串列表\n    help(obj.func)      # 查询obj.func的具体介绍和用法\n    \n#-- 测试类型的三种方法，推荐第三种\n    if type(L) == type([]):\n        print(\"L is list\")\n    if type(L) == list:\n        print(\"L is list\")\n    if isinstance(L, list):\n        print(\"L is list\")\n        \n#-- Python数据类型：哈希类型、不可哈希类型\n    # 哈希类型，即在原地不能改变的变量类型，不可变类型。可利用hash函数查看其hash值，也可以作为字典的key\n    \"数字类型：int, float, decimal.Decimal, fractions.Fraction, complex\"\n    \"字符串类型：str, bytes\"\n    \"元组：tuple\"\n    \"冻结集合：frozenset\"\n    \"布尔类型：True, False\"\n    \"None\"\n    # 不可hash类型：原地可变类型：list、dict和set。它们不可以作为字典的key。\n\n#-- 数字常量\n    1234, -1234, 0, 999999999                    # 整数\n    1.23, 1., 3.14e-10, 4E210, 4.0e+210          # 浮点数\n    0o177, 0x9ff, 0X9FF, 0b101010                # 八进制、十六进制、二进制数字\n    3+4j, 3.0+4.0j, 3J                           # 复数常量，也可以用complex(real, image)来创建\n    hex(I), oct(I), bin(I)                       # 将十进制数转化为十六进制、八进制、二进制表示的“字符串”\n    int(string, base)                            # 将字符串转化为整数，base为进制数\n    # 2.x中，有两种整数类型：一般整数（32位）和长整数（无穷精度）。可以用l或L结尾，迫使一般整数成为长整数\n    float('inf'), float('-inf'), float('nan')    # 无穷大, 无穷小, 非数\n    \n#-- 数字的表达式操作符\n    yield x                                      # 生成器函数发送协议\n    lambda args: expression                      # 生成匿名函数\n    x if y else z                                # 三元选择表达式\n    x and y, x or y, not x                       # 逻辑与、逻辑或、逻辑非\n    x in y, x not in y                           # 成员对象测试\n    x is y, x is not y                           # 对象实体测试\n    x<y, x<=y, x>y, x>=y, x==y, x!=y             # 大小比较，集合子集或超集值相等性操作符\n    1 < a < 3                                    # Python中允许连续比较\n    x|y, x&y, x^y                                # 位或、位与、位异或\n    x<<y, x>>y                                   # 位操作：x左移、右移y位\n    +, -, *, /, //, %, **                        # 真除法、floor除法：返回不大于真除法结果的整数值、取余、幂运算\n    -x, +x, ~x                                   # 一元减法、识别、按位求补（取反）\n    x[i], x[i:j:k]                               # 索引、分片、调用\n    int(3.14), float(3)                          # 强制类型转换\n    \n#-- 整数可以利用bit_length函数测试所占的位数\n    a = 1;       a.bit_length()    # 1\n    a = 1024;    a.bit_length()    # 11\n    \n#-- repr和str显示格式的区别\n    \"\"\"\n    repr格式：默认的交互模式回显，产生的结果看起来它们就像是代码。\n    str格式：打印语句，转化成一种对用户更加友好的格式。\n    \"\"\"\n    \n#-- 数字相关的模块\n    # math模块\n    # Decimal模块：小数模块\n        import decimal\n        from decimal import Decimal\n        Decimal(\"0.01\") + Decimal(\"0.02\")        # 返回Decimal(\"0.03\")\n        decimal.getcontext().prec = 4            # 设置全局精度为4 即小数点后边4位\n    # Fraction模块：分数模块\n        from fractions import Fraction\n        x = Fraction(4, 6)                       # 分数类型 4/6\n        x = Fraction(\"0.25\")                     # 分数类型 1/4 接收字符串类型的参数\n\n#-- 集合set\n    \"\"\"\n    set是一个无序不重复元素集, 基本功能包括关系测试和消除重复元素。\n    set支持union(联合), intersection(交), difference(差)和symmetric difference(对称差集)等数学运算。\n    set支持x in set, len(set), for x in set。\n    set不记录元素位置或者插入点, 因此不支持indexing, slicing, 或其它类序列的操作\n    \"\"\"\n    s = set([3,5,9,10])                          # 创建一个数值集合，返回{3, 5, 9, 10}\n    t = set(\"Hello\")                             # 创建一个唯一字符的集合返回{}\n    a = t | s;    t.union(s)                     # t 和 s的并集\n    b = t & s;    t.intersection(s)              # t 和 s的交集\n    c = t – s;    t.difference(s)                # 求差集（项在t中, 但不在s中）\n    d = t ^ s;    t.symmetric_difference(s)      # 对称差集（项在t或s中, 但不会同时出现在二者中）\n    t.add('x');   t.remove('H')                  # 增加/删除一个item\n    s.update([10,37,42])                         # 利用[......]更新s集合\n    x in s,  x not in s                          # 集合中是否存在某个值\n    s.issubset(t);      s <= t                   # 测试是否 s 中的每一个元素都在 t 中\n    s.issuperset(t);    s >= t                   # 测试是否 t 中的每一个元素都在 s 中 \n    s.copy(); \n    s.discard(x);                                # 删除s中x\n    s.clear()                                    # 清空s\n    {x**2 for x in [1, 2, 3, 4]}                 # 集合解析，结果：{16, 1, 4, 9}\n    {x for x in 'spam'}                          # 集合解析，结果：{'a', 'p', 's', 'm'}\n    \n#-- 集合frozenset，不可变对象\n    \"\"\"\n    set是可变对象，即不存在hash值，不能作为字典的键值。同样的还有list等(tuple是可以作为字典key的)\n    frozenset是不可变对象，即存在hash值，可作为字典的键值\n    frozenset对象没有add、remove等方法，但有union/intersection/difference等方法\n    \"\"\"\n    a = set([1, 2, 3])\n    b = set()\n    b.add(a)                     # error: set是不可哈希类型\n    b.add(frozenset(a))          # ok，将set变为frozenset，可哈希\n\n#-- 布尔类型bool\n    type(True)                   # 返回<class 'bool'>\n    isinstance(False, int)       # bool类型属于整型，所以返回True\n    True == 1; True is 1         # 输出(True, False)\n    \n#-- 动态类型简介\n    \"\"\"\n    变量名通过引用，指向对象。\n    Python中的“类型”属于对象，而不是变量，每个对象都包含有头部信息，比如\"类型标示符\" \"引用计数器\"等\n    \"\"\"\n    #共享引用及在原处修改：对于可变对象，要注意尽量不要共享引用！\n    #共享引用和相等测试：\n        L = [1], M = [1], L is M            # 返回False\n        L = M = [1, 2, 3], L is M           # 返回True，共享引用\n    #增强赋值和共享引用：普通+号会生成新的对象，而增强赋值+=会在原处修改\n        L = M = [1, 2]\n        L = L + [3, 4]                      # L = [1, 2, 3, 4], M = [1, 2]\n        L += [3, 4]                         # L = [1, 2, 3, 4], M = [1, 2, 3, 4]\n\n#-- 常见字符串常量和表达式\n    S = ''                                  # 空字符串\n    S = \"spam’s\"                            # 双引号和单引号相同\n    S = \"s\\np\\ta\\x00m\"                      # 转义字符\n    S = \"\"\"spam\"\"\"                          # 三重引号字符串，一般用于函数说明\n    S = r'\\temp'                            # Raw字符串，不会进行转义，抑制转义\n    S = b'Spam'                             # Python3中的字节字符串\n    S = u'spam'                             # Python2.6中的Unicode字符串\n    s1+s2, s1*3, s[i], s[i:j], len(s)       # 字符串操作\n    'a %s parrot' % 'kind'                  # 字符串格式化表达式\n    'a {1} {0} parrot'.format('kind', 'red')# 字符串格式化方法\n    for x in s: print(x)                    # 字符串迭代，成员关系\n    [x*2 for x in s]                        # 字符串列表解析\n    ','.join(['a', 'b', 'c'])               # 字符串输出，结果：a,b,c\n    \n#-- 内置str处理函数：\n    str1 = \"stringobject\"\n    str1.upper(); str1.lower(); str1.swapcase(); str1.capitalize(); str1.title()        # 全部大写，全部小写、大小写转换，首字母大写，每个单词的首字母都大写\n    str1.ljust(width)                       # 获取固定长度，左对齐，右边不够用空格补齐\n    str1.rjust(width)                       # 获取固定长度，右对齐，左边不够用空格补齐\n    str1.center(width)                      # 获取固定长度，中间对齐，两边不够用空格补齐\n    str1.zfill(width)                       # 获取固定长度，右对齐，左边不足用0补齐\n    str1.find('t',start,end)                # 查找字符串，可以指定起始及结束位置搜索\n    str1.rfind('t')                         # 从右边开始查找字符串\n    str1.count('t')                         # 查找字符串出现的次数\n    #上面所有方法都可用index代替，不同的是使用index查找不到会抛异常，而find返回-1\n    str1.replace('old','new')               # 替换函数，替换old为new，参数中可以指定maxReplaceTimes，即替换指定次数的old为new\n    str1.strip();                           # 默认删除空白符\n    str1.strip('d');                        # 删除str1字符串中开头、结尾处，位于 d 删除序列的字符\n    str1.lstrip();\n    str1.lstrip('d');                       # 删除str1字符串中开头处，位于 d 删除序列的字符\n    str1.rstrip();\n    str1.rstrip('d')                        # 删除str1字符串中结尾处，位于 d 删除序列的字符\n    str1.startswith('start')                # 是否以start开头\n    str1.endswith('end')                    # 是否以end结尾\n    str1.isalnum(); str1.isalpha(); str1.isdigit(); str1.islower(); str1.isupper()      # 判断字符串是否全为字符、数字、小写、大写\n\n#-- 三重引号编写多行字符串块，并且在代码折行处嵌入换行字符\\n\n    mantra = \"\"\"hello world\n            hello python\n            hello my friend\"\"\"\n    # mantra为\"\"\"hello world \\n hello python \\n hello my friend\"\"\"\n    \n#-- 索引和分片：\n    S[0], S[len(S)–1], S[-1]                # 索引\n    S[1:3], S[1:], S[:-1], S[1:10:2]        # 分片，第三个参数指定步长，如`S[1:10:2]`是从1位到10位没隔2位获取一个字符。\n\n#-- 字符串转换工具：\n    int('42'), str(42)                      # 返回(42, '42')\n    float('4.13'), str(4.13)                # 返回(4.13, '4.13')\n    ord('s'), chr(115)                      # 返回(115, 's')\n    int('1001', 2)                          # 将字符串作为二进制数字，转化为数字，返回9\n    bin(13), oct(13), hex(13)               # 将整数转化为二进制/八进制/十六进制字符串，返回('0b1101', '015', '0xd')\n    \n#-- 另类字符串连接\n    name = \"wang\" \"hong\"                    # 单行，name = \"wanghong\"\n    name = \"wang\" \\\n            \"hong\"                          # 多行，name = \"wanghong\"\n\n#-- Python中的字符串格式化实现1--字符串格式化表达式\n    \"\"\"\n    基于C语言的'print'模型，并且在大多数的现有的语言中使用。\n    通用结构：%[(name)][flag][width].[precision]typecode\n    \"\"\"\n    \"this is %d %s bird\" % (1, 'dead')                          # 一般的格式化表达式\n    \"%s---%s---%s\" % (42, 3.14, [1, 2, 3])                      # 字符串输出：'42---3.14---[1, 2, 3]'\n    \"%d...%6d...%-6d...%06d\" % (1234, 1234, 1234, 1234)         # 对齐方式及填充：\"1234...  1234...1234  ...001234\"\n    x = 1.23456789\n    \"%e | %f | %g\" % (x, x, x)                                  # 对齐方式：\"1.234568e+00 | 1.234568 | 1.23457\"\n    \"%6.2f*%-6.2f*%06.2f*%+6.2f\" % (x, x, x, x)                 # 对齐方式：'  1.23*1.23  *001.23* +1.23'\n    \"%(name1)d---%(name2)s\" % {\"name1\":23, \"name2\":\"value2\"}    # 基于字典的格式化表达式\n    \"%(name)s is %(age)d\" % vars()                              # vars()函数调用返回一个字典，包含了所有本函数调用时存在的变量\n    \n#-- Python中的字符串格式化实现2--字符串格式化调用方法\n    # 普通调用\n    \"{0}, {1} and {2}\".format('spam', 'ham', 'eggs')            # 基于位置的调用\n    \"{motto} and {pork}\".format(motto = 'spam', pork = 'ham')   # 基于Key的调用\n    \"{motto} and {0}\".format('ham', motto = 'spam')             # 混合调用\n    # 添加键 属性 偏移量 (import sys)\n    \"my {1[spam]} runs {0.platform}\".format(sys, {'spam':'laptop'})                 # 基于位置的键和属性\n    \"{config[spam]} {sys.platform}\".format(sys = sys, config = {'spam':'laptop'})   # 基于Key的键和属性\n    \"first = {0[0]}, second = {0[1]}\".format(['A', 'B', 'C'])                       # 基于位置的偏移量\n    # 具体格式化\n    \"{0:e}, {1:.3e}, {2:g}\".format(3.14159, 3.14159, 3.14159)   # 输出'3.141590e+00, 3.142e+00, 3.14159'\n    \"{fieldname:format_spec}\".format(......)\n    # 说明:\n    \"\"\"\n        fieldname是指定参数的一个数字或关键字, 后边可跟可选的\".name\"或\"[index]\"成分引用\n        format_spec ::=  [[fill]align][sign][#][0][width][,][.precision][type]\n        fill        ::=  <any character>              #填充字符\n        align       ::=  \"<\" | \">\" | \"=\" | \"^\"        #对齐方式\n        sign        ::=  \"+\" | \"-\" | \" \"              #符号说明\n        width       ::=  integer                      #字符串宽度\n        precision   ::=  integer                      #浮点数精度\n        type        ::=  \"b\" | \"c\" | \"d\" | \"e\" | \"E\" | \"f\" | \"F\" | \"g\" | \"G\" | \"n\" | \"o\" | \"s\" | \"x\" | \"X\" | \"%\"\n    \"\"\"\n    # 例子:\n        '={0:10} = {1:10}'.format('spam', 123.456)    # 输出'=spam       =    123.456'\n        '={0:>10}='.format('test')                    # 输出'=      test='\n        '={0:<10}='.format('test')                    # 输出'=test      ='\n        '={0:^10}='.format('test')                    # 输出'=   test   ='\n        '{0:X}, {1:o}, {2:b}'.format(255, 255, 255)   # 输出'FF, 377, 11111111'\n        'My name is {0:{1}}.'.format('Fred', 8)       # 输出'My name is Fred    .'  动态指定参数\n\n#-- 常用列表常量和操作\n    L = [[1, 2], 'string', {}]                        # 嵌套列表\n    L = list('spam')                                  # 列表初始化\n    L = list(range(0, 4))                             # 列表初始化\n    list(map(ord, 'spam'))                            # 列表解析\n    len(L)                                            # 求列表长度\n    L.count(value)                                    # 求列表中某个值的个数\n    L.append(obj)                                     # 向列表的尾部添加数据，比如append(2)，添加元素2\n    L.insert(index, obj)                              # 向列表的指定index位置添加数据，index及其之后的数据后移\n    L.extend(interable)                               # 通过添加iterable中的元素来扩展列表，比如extend([2])，添加元素2，注意和append的区别\n    L.index(value, [start, [stop]])                   # 返回列表中值value的第一个索引\n    L.pop([index])                                    # 删除并返回index处的元素，默认为删除并返回最后一个元素\n    L.remove(value)                                   # 删除列表中的value值，只删除第一次出现的value的值\n    L.reverse()                                       # 反转列表\n    L.sort(cmp=None, key=None, reverse=False)         # 排序列表\n    a = [1, 2, 3], b = a[10:]                         # 注意，这里不会引发IndexError异常，只会返回一个空的列表[]\n    a = [], a += [1]                                  # 这里实在原有列表的基础上进行操作，即列表的id没有改变\n    a = [], a = a + [1]                               # 这里最后的a要构建一个新的列表，即a的id发生了变化\n     \n#-- 用切片来删除序列的某一段\n    a = [1, 2, 3, 4, 5, 6, 7]\n    a[1:4] = []                                       # a = [1, 5, 6, 7]\n    a = [0, 1, 2, 3, 4, 5, 6, 7]\n    del a[::2]                                        # 去除偶数项(偶数索引的)，a = [1, 3, 5, 7]\n    \n#-- 常用字典常量和操作\n    D = {}\n    D = {'spam':2, 'tol':{'ham':1}}                   # 嵌套字典\n    D = dict.fromkeys(['s', 'd'], 8)                  # {'s': 8, 'd': 8}\n    D = dict(name = 'tom', age = 12)                  # {'age': 12, 'name': 'tom'}\n    D = dict([('name', 'tom'), ('age', 12)])          # {'age': 12, 'name': 'tom'}\n    D = dict(zip(['name', 'age'], ['tom', 12]))       # {'age': 12, 'name': 'tom'}\n    D.keys(); D.values(); D.items()                   # 字典键、值以及键值对\n    D.get(key, default)                               # get函数\n    D.update(D_other)                                 # 合并字典，如果存在相同的键值，D_other的数据会覆盖掉D的数据\n    D.pop(key, [D])                                   # 删除字典中键值为key的项，返回键值为key的值，如果不存在，返回默认值D，否则异常\n    D.popitem()                                       # pop字典中随机的一项（一个键值对）\n    D.setdefault(k[, d])                              # 设置D中某一项的默认值。如果k存在，则返回D[k]，否则设置D[k]=d，同时返回D[k]。\n    del D                                             # 删除字典\n    del D['key']                                      # 删除字典的某一项\n    if key in D:   if key not in D:                   # 测试字典键是否存在\n    # 字典注意事项：（1）对新索引赋值会添加一项（2）字典键不一定非得是字符串，也可以为任何的不可变对象\n    # 不可变对象：调用对象自身的任意方法，也不会改变该对象自身的内容，这些方法会创建新的对象并返回。\n    # 字符串、整数、tuple都是不可变对象，dict、set、list都是可变对象\n    D[(1,2,3)] = 2                                    # tuple作为字典的key\n\n#-- 字典解析\n    D = {k:8 for k in ['s', 'd']}                     # {'s': 8, 'd': 8}\n    D = {k:v for (k, v) in zip(['name', 'age'], ['tom', 12])}       # {'age': 12, 'name': tom}\n    \n#-- 字典的特殊方法__missing__：当查找找不到key时，会执行该方法\n    class Dict(dict):\n        def __missing__(self, key):\n            self[key] = []\n            return self[key]\n    dct = dict()\n    dct[\"foo\"].append(1)    # 这有点类似于collections.defalutdict\n    dct[\"foo\"]              # [1]\n    \n#-- 元组和列表的唯一区别在于元组是不可变对象，列表是可变对象\n    a = [1, 2, 3]           # a[1] = 0, OK\n    a = (1, 2, 3)           # a[1] = 0, Error\n    a = ([1, 2])            # a[0][1] = 0, OK\n    a = [(1, 2)]            # a[0][1] = 0, Error\n    \n#-- 元组的特殊语法: 逗号和圆括号\n    D = (12)                # 此时D为一个整数 即D = 12\n    D = (12, )              # 此时D为一个元组 即D = (12, )\n    \n#-- 文件基本操作\n    output = open(r'C:\\spam', 'w')          # 打开输出文件，用于写\n    input = open('data', 'r')               # 打开输入文件，用于读。打开的方式可以为'w', 'r', 'a', 'wb', 'rb', 'ab'等\n    fp.read([size])                         # size为读取的长度，以byte为单位\n    fp.readline([size])                     # 读一行，如果定义了size，有可能返回的只是一行的一部分\n    fp.readlines([size])                    # 把文件每一行作为一个list的一个成员，并返回这个list。其实它的内部是通过循环调用readline()来实现的。如果提供size参数，size是表示读取内容的总长。\n    fp.readable()                           # 是否可读\n    fp.write(str)                           # 把str写到文件中，write()并不会在str后加上一个换行符\n    fp.writelines(seq)                      # 把seq的内容全部写到文件中(多行一次性写入)\n    fp.writeable()                          # 是否可写\n    fp.close()                              # 关闭文件。\n    fp.flush()                              # 把缓冲区的内容写入硬盘\n    fp.fileno()                             # 返回一个长整型的”文件标签“\n    fp.isatty()                             # 文件是否是一个终端设备文件（unix系统中的）\n    fp.tell()                               # 返回文件操作标记的当前位置，以文件的开头为原点\n    fp.next()                               # 返回下一行，并将文件操作标记位移到下一行。把一个file用于for … in file这样的语句时，就是调用next()函数来实现遍历的。\n    fp.seek(offset[,whence])                # 将文件打开操作标记移到offset的位置。whence为0表示从头开始计算，1表示以当前位置为原点计算。2表示以文件末尾为原点进行计算。\n    fp.seekable()                           # 是否可以seek\n    fp.truncate([size])                     # 把文件裁成规定的大小，默认的是裁到当前文件操作标记的位置。\n    for line in open('data'): \n        print(line)                         # 使用for语句，比较适用于打开比较大的文件\n    with open('data') as file:\n        print(file.readline())              # 使用with语句，可以保证文件关闭\n    with open('data') as file:\n        lines = file.readlines()            # 一次读入文件所有行，并关闭文件\n    open('f.txt', encoding = 'latin-1')     # Python3.x Unicode文本文件\n    open('f.bin', 'rb')                     # Python3.x 二进制bytes文件\n    # 文件对象还有相应的属性：buffer closed encoding errors line_buffering name newlines等\n    \n#-- 其他\n    # Python中的真假值含义：1. 数字如果非零，则为真，0为假。 2. 其他对象如果非空，则为真\n    # 通常意义下的类型分类：1. 数字、序列、映射。 2. 可变类型和不可变类型\n\n\n\"\"\"语法和语句----语法和语句----语法和语句----语法和语句----语法和语句----语法和语句----语法和语句----语法和语句----语法和语句----语法和语句----语法和语句\"\"\"\n\n#-- 赋值语句的形式\n    spam = 'spam'                          # 基本形式\n    spam, ham = 'spam', 'ham'              # 元组赋值形式\n    [spam, ham] = ['s', 'h']               # 列表赋值形式\n    a, b, c, d = 'abcd'                    # 序列赋值形式\n    a, *b, c = 'spam'                      # 序列解包形式（Python3.x中才有）\n    spam = ham = 'no'                      # 多目标赋值运算，涉及到共享引用\n    spam += 42                             # 增强赋值，涉及到共享引用\n\n#-- 序列赋值 序列解包\n    [a, b, c] = (1, 2, 3)                  # a = 1, b = 2, c = 3\n    a, b, c, d = \"spam\"                    # a = 's', b = 'p', c = 'a', d = 'm'\n    a, b, c = range(3)                     # a = 0, b = 1, c = 2\n    a, *b = [1, 2, 3, 4]                   # a = 1, b = [2, 3, 4]\n    *a, b = [1, 2, 3, 4]                   # a = [1, 2, 3], b = 4\n    a, *b, c = [1, 2, 3, 4]                # a = 1, b = [2, 3], c = 4\n    # 带有*时 会优先匹配*之外的变量 如\n    a, *b, c = [1, 2]                      # a = 1, c = 2, b = []\n\n#-- print函数原型\n    print(value, ..., sep=' ', end='\\n', file=sys.stdout, flush=False)\n    # 流的重定向\n    print('hello world')                   # 等于sys.stdout.write('hello world')\n    temp = sys.stdout                      # 原有流的保存\n    sys.stdout = open('log.log', 'a')      # 流的重定向\n    print('hello world')                   # 写入到文件log.log\n    sys.stdout.close()\n    sys.stdout = temp                      # 原有流的复原\n    \n#-- Python中and或or总是返回对象(左边的对象或右边的对象) 且具有短路求值的特性\n    1 or 2 or 3                            # 返回 1\n    1 and 2 and 3                          # 返回 3\n\n#-- if/else三元表达符（if语句在行内）\n    A = 1 if X else 2\n    A = 1 if X else (2 if Y else 3)\n    # 也可以使用and-or语句（一条语句实现多个if-else）\n    a = 6\n    result = (a > 20 and \"big than 20\" or a > 10 and \"big than 10\" or a > 5 and \"big than 5\")    # 返回\"big than 5\"\n\n#-- Python的while语句或者for语句可以带else语句 当然也可以带continue/break/pass语句\n    while a > 1:\n        anything\n    else:\n        anything\n    # else语句会在循环结束后执行，除非在循环中执行了break，同样的还有for语句\n    for i in range(5):\n        anything\n    else:\n        anything\n\n#-- for循环的元组赋值\n    for (a, b) in [(1, 2), (3, 4)]:                   # 最简单的赋值\n    for ((a, b), c) in [((1, 2), 3), ((4, 5), 6)]:    # 自动解包赋值\n    for ((a, b), c) in [((1, 2), 3), (\"XY\", 6)]:      # 自动解包 a = X, b = Y, c = 6\n    for (a, *b) in [(1, 2, 3), (4, 5, 6)]:            # 自动解包赋值\n\n#-- 列表解析语法\n    M = [[1,2,3], [4,5,6], [7,8,9]]\n    res = [sum(row) for row in M]                     # G = [6, 15, 24] 一般的列表解析 生成一个列表\n    res = [c * 2 for c in 'spam']                     # ['ss', 'pp', 'aa', 'mm']\n    res = [a * b for a in [1, 2] for b in [4, 5]]     # 多解析过程 返回[4, 5, 8, 10]\n    res = [a for a in [1, 2, 3] if a < 2]             # 带判断条件的解析过程\n    res = [a if a > 0 else 0 for a in [-1, 0, 1]]     # 带判断条件的高级解析过程\n    # 两个列表同时解析：使用zip函数\n    for teama, teamb in zip([\"Packers\", \"49ers\"], [\"Ravens\", \"Patriots\"]):\n        print(teama + \" vs. \" + teamb)\n    # 带索引的列表解析：使用enumerate函数\n    for index, team in enumerate([\"Packers\", \"49ers\", \"Ravens\", \"Patriots\"]):\n        print(index, team)                            # 输出0, Packers \\n 1, 49ers \\n ......\n    \n#-- 生成器表达式\n    G = (sum(row) for row in M)                       # 使用小括号可以创建所需结果的生成器generator object\n    next(G), next(G), next(G)                         # 输出(6, 15, 24)\n    G = {sum(row) for row in M}                       # G = {6, 15, 24} 解析语法还可以生成集合和字典\n    G = {i:sum(M[i]) for i in range(3)}               # G = {0: 6, 1: 15, 2: 24}\n\n#-- 文档字符串:出现在Module的开端以及其中函数或类的开端 使用三重引号字符串\n    \"\"\"\n    module document\n    \"\"\"\n    def func():\n        \"\"\"\n        function document\n        \"\"\"\n        print()\n    class Employee(object):\n        \"\"\"\n        class document\n        \"\"\"\n        print()\n    print(func.__doc__)                # 输出函数文档字符串\n    print(Employee.__doc__)            # 输出类的文档字符串\n    \n#-- 命名惯例:\n    \"\"\"\n    以单一下划线开头的变量名(_X)不会被from module import*等语句导入\n    前后有两个下划线的变量名(__X__)是系统定义的变量名，对解释器有特殊意义\n    以两个下划线开头但不以下划线结尾的变量名(__X)是类的本地(私有)变量\n    \"\"\"\n\n#-- 列表解析 in成员关系测试 map sorted zip enumerate内置函数等都使用了迭代协议\n    'first line' in open('test.txt')   # in测试 返回True或False\n    list(map(str.upper, open('t')))    # map内置函数\n    sorted(iter([2, 5, 8, 3, 1]))      # sorted内置函数\n    list(zip([1, 2], [3, 4]))          # zip内置函数 [(1, 3), (2, 4)]\n\n#-- del语句: 手动删除某个变量\n    del X\n\n#-- 获取列表的子表的方法:\n    x = [1,2,3,4,5,6]\n    x[:3]                              # 前3个[1,2,3]\n    x[1:5]                             # 中间4个[2,3,4,5]\n    x[-3:]                             # 最后3个[4,5,6]\n    x[::2]                             # 奇数项[1,3,5]\n    x[1::2]                            # 偶数项[2,4,6]\n    \n#-- 手动迭代：iter和next\n    L = [1, 2]\n    I = iter(L)                        # I为L的迭代器\n    I.next()                           # 返回1\n    I.next()                           # 返回2\n    I.next()                           # Error:StopIteration\n    \n#-- Python中的可迭代对象\n    \"\"\"\n    1.range迭代器\n    2.map、zip和filter迭代器\n    3.字典视图迭代器：D.keys()), D.items()等\n    4.文件类型\n    \"\"\"\n\n\n\"\"\"函数语法规则----函数语法规则----函数语法规则----函数语法规则----函数语法规则----函数语法规则----函数语法规则----函数语法规则----函数语法规则----函数语法规则\"\"\"\n\n#-- 函数相关的语句和表达式\n    myfunc('spam')                     # 函数调用\n    def myfunc():                      # 函数定义\n    return None                        # 函数返回值\n    global a                           # 全局变量\n    nonlocal x                         # 在函数或其他作用域中使用外层（非全局）变量\n    yield x                            # 生成器函数返回\n    lambda                             # 匿名函数\n    \n#-- Python函数变量名解析:LEGB原则，即:\n    \"\"\"\n    local(functin) --> encloseing function locals --> global(module) --> build-in(python)\n    说明:以下边的函数maker为例 则相对于action而言 X为Local N为Encloseing\n    \"\"\"\n\n#-- 嵌套函数举例:工厂函数\n    def maker(N):\n        def action(X):\n            return X ** N\n        return action\n    f = maker(2)                       # pass 2 to N\n    f(3)                               # 9, pass 3 to X\n\n#-- 嵌套函数举例:lambda实例\n    def maker(N):\n        action = (lambda X: X**N)\n        return action\n    f = maker(2)                       # pass 2 to N\n    f(3)                               # 9, pass 3 to X\n\n#-- nonlocal和global语句的区别\n    # nonlocal应用于一个嵌套的函数的作用域中的一个名称 例如:\n    start = 100\n    def tester(start):\n        def nested(label):\n            nonlocal start             # 指定start为tester函数内的local变量 而不是global变量start\n            print(label, start)\n            start += 3\n        return nested\n    # global为全局的变量 即def之外的变量\n    def tester(start):\n        def nested(label):\n            global start               # 指定start为global变量start\n            print(label, start)\n            start += 3\n        return nested    \n    \n#-- 函数参数，不可变参数通过“值”传递，可变参数通过“引用”传递\n    def f(a, b, c): print(a, b, c)\n    f(1, 2, 3)                         # 参数位置匹配\n    f(1, c = 3, b = 2)                 # 参数关键字匹配\n    def f(a, b=1, c=2): print(a, b, c)\n    f(1)                               # 默认参数匹配\n    f(1, 2)                            # 默认参数匹配\n    f(a = 1, c = 3)                    # 关键字参数和默认参数的混合\n    # Keyword-Only参数:出现在*args之后 必须用关键字进行匹配\n    def keyOnly(a, *b, c): print('')   # c就为keyword-only匹配 必须使用关键字c = value匹配\n    def keyOnly(a, *, b, c): ......    # b c为keyword-only匹配 必须使用关键字匹配\n    def keyOnly(a, *, b = 1): ......   # b有默认值 或者省略 或者使用关键字参数b = value\n\n#-- 可变参数匹配: * 和 **\n    def f(*args): print(args)          # 在元组中收集不匹配的位置参数\n    f(1, 2, 3)                         # 输出(1, 2, 3)\n    def f(**args): print(args)         # 在字典中收集不匹配的关键字参数\n    f(a = 1, b = 2)                    # 输出{'a':1, 'b':2}\n    def f(a, *b, **c): print(a, b, c)  # 两者混合使用\n    f(1, 2, 3, x=4, y=5)               # 输出1, (2, 3), {'x':4, 'y':5}\n    \n#-- 函数调用时的参数解包: * 和 ** 分别解包元组和字典\n    func(1, *(2, 3))  <==>  func(1, 2, 3)\n    func(1, **{'c':3, 'b':2})  <==>  func(1, b = 2, c = 3)\n    func(1, *(2, 3), **{'c':3, 'b':2})  <==>  func(1, 2, 3, b = 2, c = 3)\n    \n#-- 函数属性:(自己定义的)函数可以添加属性\n    def func():.....\n    func.count = 1                     # 自定义函数添加属性\n    print.count = 1                    # Error 内置函数不可以添加属性\n    \n#-- 函数注解: 编写在def头部行 主要用于说明参数范围、参数类型、返回值类型等\n    def func(a:'spam', b:(1, 10), c:float) -> int :\n        print(a, b, c)\n    func.__annotations__               # {'c':<class 'float'>, 'b':(1, 10), 'a':'spam', 'return':<class 'int'>}\n    # 编写注解的同时 还是可以使用函数默认值 并且注解的位置位于=号的前边\n    def func(a:'spam'='a', b:(1, 10)=2, c:float=3) -> int :\n        print(a, b, c)\n\n#-- 匿名函数:lambda\n    f = lambda x, y, z : x + y + z     # 普通匿名函数，使用方法f(1, 2, 3)\n    f = lambda x = 1, y = 1: x + y     # 带默认参数的lambda函数\n    def action(x):                     # 嵌套lambda函数\n        return (lambda y : x + y)\n    f = lambda: a if xxx() else b      # 无参数的lambda函数，使用方法f()\n\n#-- lambda函数与map filter reduce函数的结合\n    list(map((lambda x: x + 1), [1, 2, 3]))              # [2, 3, 4]\n    list(filter((lambda x: x > 0), range(-4, 5)))        # [1, 2, 3, 4]\n    functools.reduce((lambda x, y: x + y), [1, 2, 3])    # 6\n    functools.reduce((lambda x, y: x * y), [2, 3, 4])    # 24\n    \n#-- 生成器函数:yield VS return\n    def gensquare(N):\n        for i in range(N):\n            yield i** 2                # 状态挂起 可以恢复到此时的状态\n    for i in gensquare(5):             # 使用方法\n        print(i, end = ' ')            # [0, 1, 4, 9, 16]\n    x = gensquare(2)                   # x是一个生成对象\n    next(x)                            # 等同于x.__next__() 返回0\n    next(x)                            # 等同于x.__next__() 返回1\n    next(x)                            # 等同于x.__next__() 抛出异常StopIteration\n    \n#-- 生成器表达式:小括号进行列表解析\n    G = (x ** 2 for x in range(3))     # 使用小括号可以创建所需结果的生成器generator object\n    next(G), next(G), next(G)          # 和上述中的生成器函数的返回值一致\n    #（1）生成器(生成器函数/生成器表达式)是单个迭代对象\n    G = (x ** 2 for x in range(4))\n    I1 = iter(G)                       # 这里实际上iter(G) = G\n    next(I1)                           # 输出0\n    next(G)                            # 输出1\n    next(I1)                           # 输出4\n    #（2）生成器不保留迭代后的结果\n    gen = (i for i in range(4))\n    2 in gen                           # 返回True\n    3 in gen                           # 返回True\n    1 in gen                           # 返回False，其实检测2的时候，1已经就不在生成器中了，即1已经被迭代过了，同理2、3也不在了\n\n#-- 本地变量是静态检测的\n    X = 22                             # 全局变量X的声明和定义\n    def test():\n        print(X)                       # 如果没有下一语句 则该句合法 打印全局变量X\n        X = 88                         # 这一语句使得上一语句非法 因为它使得X变成了本地变量 上一句变成了打印一个未定义的本地变量(局部变量)\n        if False:                      # 即使这样的语句 也会把print语句视为非法语句 因为:\n            X = 88                     # Python会无视if语句而仍然声明了局部变量X\n    def test():                        # 改进\n        global X                       # 声明变量X为全局变量\n        print(X)                       # 打印全局变量X\n        X = 88                         # 改变全局变量X\n        \n#-- 函数的默认值是在函数定义的时候实例化的 而不是在调用的时候 例子:\n    def foo(numbers=[]):               # 这里的[]是可变的\n        numbers.append(9)    \n        print(numbers)\n    foo()                              # first time, like before, [9]\n    foo()                              # second time, not like before, [9, 9]\n    foo()                              # third time, not like before too, [9, 9, 9]\n    # 改进:\n    def foo(numbers=None):\n        if numbers is None: numbers = []\n        numbers.append(9)\n        print(numbers)\n    # 另外一个例子 参数的默认值为不可变的:\n    def foo(count=0):                  # 这里的0是数字, 是不可变的\n        count += 1\n        print(count)\n    foo()                              # 输出1\n    foo()                              # 还是输出1\n    foo(3)                             # 输出4\n    foo()                              # 还是输出1\n    \n\n\"\"\"函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子\"\"\"\n\n    \"\"\"数学运算类\"\"\"\n    abs(x)                              # 求绝对值，参数可以是整型，也可以是复数，若参数是复数，则返回复数的模\n    complex([real[, imag]])             # 创建一个复数\n    divmod(a, b)                        # 分别取商和余数，注意：整型、浮点型都可以\n    float([x])                          # 将一个字符串或数转换为浮点数。如果无参数将返回0.0\n    int([x[, base]])                    # 将一个字符串或浮点数转换为int类型，base表示进制\n    long([x[, base]])                   # 将一个字符串或浮点数转换为long类型\n    pow(x, y)                           # 返回x的y次幂\n    range([start], stop[, step])        # 产生一个序列，默认从0开始\n    round(x[, n])                       # 四舍五入\n    sum(iterable[, start])              # 对集合求和\n    oct(x)                              # 将一个数字转化为8进制字符串\n    hex(x)                              # 将一个数字转换为16进制字符串\n    chr(i)                              # 返回给定int类型对应的ASCII字符\n    unichr(i)                           # 返回给定int类型的unicode\n    ord(c)                              # 返回ASCII字符对应的整数\n    bin(x)                              # 将整数x转换为二进制字符串\n    bool([x])                           # 将x转换为Boolean类型\n    \n    \"\"\"集合类操作\"\"\"\n    basestring()                        # str和unicode的超类，不能直接调用，可以用作isinstance判断\n    format(value [, format_spec])       # 格式化输出字符串，格式化的参数顺序从0开始，如“I am {0},I like {1}”\n    enumerate(sequence[, start=0])      # 返回一个可枚举的对象，注意它有第二个参数\n    iter(obj[, sentinel])               # 生成一个对象的迭代器，第二个参数表示分隔符\n    max(iterable[, args...][key])       # 返回集合中的最大值\n    min(iterable[, args...][key])       # 返回集合中的最小值\n    dict([arg])                         # 创建数据字典\n    list([iterable])                    # 将一个集合类转换为另外一个集合类\n    set()                               # set对象实例化\n    frozenset([iterable])               # 产生一个不可变的set\n    tuple([iterable])                   # 生成一个tuple类型\n    str([object])                       # 转换为string类型\n    sorted(iterable[, cmp[, key[, reverse]]])             # 集合排序\n        L = [('b',2),('a',1),('c',3),('d',4)]\n        sorted(L, key=lambda x: x[1]), reverse=True)      # 使用Key参数和reverse参数\n        sorted(L, key=lambda x: (x[0], x[1]))             # 使用key参数进行多条件排序，即如果x[0]相同，则比较x[1]\n\n    \"\"\"逻辑判断\"\"\"\n    all(iterable)                       # 集合中的元素都为真的时候为真，特别的，若为空串返回为True\n    any(iterable)                       # 集合中的元素有一个为真的时候为真，特别的，若为空串返回为False\n    cmp(x, y)                           # 如果x < y ,返回负数；x == y, 返回0；x > y,返回正数\n\n    \"\"\"IO操作\"\"\"\n    file(filename [, mode [, bufsize]]) # file类型的构造函数。\n    input([prompt])                     # 获取用户输入，推荐使用raw_input，因为该函数将不会捕获用户的错误输入，意思是自行判断类型\n    # 在 Built-in Functions 里有一句话是这样写的：Consider using the raw_input() function for general input from users.\n    raw_input([prompt])                 # 设置输入，输入都是作为字符串处理\n    open(name[, mode[, buffering]])     # 打开文件，与file有什么不同？推荐使用open\n    \n    \"\"\"其他\"\"\"\n    callable(object)                    # 检查对象object是否可调用\n    classmethod(func)                   # 用来说明这个func是个类方法\n    staticmethod(func)                  # 用来说明这个func为静态方法\n    dir([object])                       # 不带参数时，返回当前范围内的变量、方法和定义的类型列表；带参数时，返回参数的属性、方法列表。\n    help(obj)                           # 返回obj的帮助信息\n    eval(expression)                    # 计算表达式expression的值，并返回\n    exec(str)                           # 将str作为Python语句执行\n    execfile(filename)                  # 用法类似exec()，不同的是execfile的参数filename为文件名，而exec的参数为字符串。\n    filter(function, iterable)          # 构造一个序列，等价于[item for item in iterable if function(item)]，function返回值为True或False的函数\n        list(filter(bool, range(-3, 4)))# 返回[-3, -2, -1, 1, 2, 3], 没有0\n    hasattr(object, name)               # 判断对象object是否包含名为name的特性\n    getattr(object, name [, defalut])   # 获取一个类的属性\n    setattr(object, name, value)        # 设置属性值\n    delattr(object, name)               # 删除object对象名为name的属性\n    globals()                           # 返回一个描述当前全局符号表的字典\n    hash(object)                        # 如果对象object为哈希表类型，返回对象object的哈希值\n    id(object)                          # 返回对象的唯一标识，一串数字\n    isinstance(object, classinfo)       # 判断object是否是class的实例\n        isinstance(1, int)              # 判断是不是int类型\n        isinstance(1, (int, float))     # isinstance的第二个参数接受一个元组类型\n    issubclass(class, classinfo)        # 判断class是否为classinfo的子类\n    locals()                            # 返回当前的变量列表\n    map(function, iterable, ...)        # 遍历每个元素，执行function操作\n        list(map(abs, range(-3, 4)))    # 返回[3, 2, 1, 0, 1, 2, 3]\n    next(iterator[, default])           # 类似于iterator.next()\n    property([fget[, fset[, fdel[, doc]]]])           # 属性访问的包装类，设置后可以通过c.x=value等来访问setter和getter\n    reduce(function, iterable[, initializer])         # 合并操作，从第一个开始是前两个参数，然后是前两个的结果与第三个合并进行处理，以此类推\n        def add(x,y):return x + y \n        reduce(add, range(1, 11))                     # 返回55 (注:1+2+3+4+5+6+7+8+9+10 = 55)\n        reduce(add, range(1, 11), 20)                 # 返回75\n    reload(module)                      # 重新加载模块\n    repr(object)                        # 将一个对象变幻为可打印的格式\n    slice(start, stop[, step])          # 产生分片对象\n    type(object)                        # 返回该object的类型\n    vars([object])                      # 返回对象的变量名、变量值的字典\n        a = Class();                    # Class为一个空类\n        a.name = 'qi', a.age = 9\n        vars(a)                         # {'name':'qi', 'age':9}\n    zip([iterable, ...])                # 返回对应数组\n        list(zip([1, 2, 3], [4, 5, 6])) # [(1, 4), (2, 5), (3, 6)]\n        a = [1, 2, 3],  b = [\"a\", \"b\", \"c\"]\n        z = zip(a, b)                   # 压缩：[(1, \"a\"), (2, \"b\"), (3, \"c\")]\n        zip(*z)                         # 解压缩：[(1, 2, 3), (\"a\", \"b\", \"c\")]\n    unicode(string, encoding, errors)   # 将字符串string转化为unicode形式，string为encoded string。\n\n    \n\"\"\"模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle\"\"\"\n\n#-- Python模块搜索路径:\n    \"\"\"\n    (1)程序的主目录    (2)PYTHONPATH目录 (3)标准链接库目录 (4)任何.pth文件的内容\n    \"\"\"\n    \n#-- 查看全部的模块搜索路径\n    import sys\n    sys.path\n    sys.argv                            # 获得脚本的参数\n    sys.builtin_module_names            # 查找内建模块\n    sys.platform                        # 返回当前平台 出现如： \"win32\" \"linux\" \"darwin\"等\n    sys.modules                         # 查找已导入的模块\n    sys.modules.keys()\n    sys.stdout                          # stdout 和 stderr 都是类文件对象，但是它们都是只写的。它们都没有 read 方法，只有 write 方法\n    sys.stdout.write(\"hello\")\n    sys.stderr\n    sys.stdin   \n\n#-- 模块的使用代码\n    import module1, module2             # 导入module1 使用module1.printer()\n    from module1 import printer         # 导入module1中的printer变量 使用printer()\n    from module1 import *               # 导入module1中的全部变量 使用不必添加module1前缀\n\n#-- 重载模块reload: 这是一个内置函数 而不是一条语句\n    from imp import reload\n    reload(module)\n    \n#-- 模块的包导入:使用点号(.)而不是路径(dir1\\dir2)进行导入\n    import dir1.dir2.mod                # d导入包(目录)dir1中的包dir2中的mod模块 此时dir1必须在Python可搜索路径中\n    from dir1.dir2.mod import *         # from语法的包导入\n\n#-- __init__.py包文件:每个导入的包中都应该包含这么一个文件\n    \"\"\"\n    该文件可以为空\n    首次进行包导入时 该文件会自动执行\n    高级功能:在该文件中使用__all__列表来定义包(目录)以from*的形式导入时 需要导入什么\n    \"\"\"\n    \n#-- 包相对导入:使用点号(.) 只能使用from语句\n    from . import spam                  # 导入当前目录下的spam模块（Python2: 当前目录下的模块, 直接导入即可）\n    from .spam import name              # 导入当前目录下的spam模块的name属性（Python2: 当前目录下的模块, 直接导入即可，不用加.）\n    from .. import spam                 # 导入当前目录的父目录下的spam模块\n    \n#-- 包相对导入与普通导入的区别\n    from string import *                # 这里导入的string模块为sys.path路径上的 而不是本目录下的string模块(如果存在也不是)\n    from .string import *               # 这里导入的string模块为本目录下的(不存在则导入失败) 而不是sys.path路径上的\n    \n#-- 模块数据隐藏:最小化from*的破坏\n    _X                                  # 变量名前加下划线可以防止from*导入时该变量名被复制出去\n    __all__ = ['x', 'x1', 'x2']         # 使用__all__列表指定from*时复制出去的变量名(变量名在列表中为字符串形式)\n\n#-- 可以使用__name__进行模块的单元测试:当模块为顶层执行文件时值为'__main__' 当模块被导入时为模块名\n    if __name__ == '__main__':\n        doSomething\n    # 模块属性中还有其他属性，例如：\n    __doc__                             # 模块的说明文档\n    __file__                            # 模块文件的文件名，包括全路径\n    __name__                            # 主文件或者被导入文件\n    __package__                         # 模块所在的包\n        \n#-- import语句from语句的as扩展\n    import modulename as name\n    from modulename import attrname as name\n    \n#-- 得到模块属性的几种方法 假设为了得到name属性的值\n    M.name\n    M.__dict__['name']\n    sys.modules['M'].name\n    getattr(M, 'name')\n    \n\n\"\"\"类与面向对象----类与面向对象----类与面向对象----类与面向对象----类与面向对象----类与面向对象----类与面向对象----类与面向对象----类与面向对象----类与面向对象\"\"\"\n\n#-- 最普通的类\n    class C1(C2, C3):\n        spam = 42                       # 数据属性\n        def __init__(self, name):       # 函数属性:构造函数\n            self.name = name\n        def __del__(self):              # 函数属性:析构函数\n            print(\"goodbey \", self.name)    \n    I1 = C1('bob')\n    \n#-- Python的类没有基于参数的函数重载\n    class FirstClass(object):\n        def test(self, string):\n            print(string)\n        def test(self):                 # 此时类中只有一个test函数 即后者test(self) 它覆盖掉前者带参数的test函数\n            print(\"hello world\")\n\n#-- 子类扩展超类: 尽量调用超类的方法\n    class Manager(Person):\n        def giveRaise(self, percent, bonus = .10):\n            self.pay = int(self.pay*(1 + percent + bonus))     # 不好的方式 复制粘贴超类代码\n            Person.giveRaise(self, percent + bonus)            # 好的方式 尽量调用超类方法\n\n#-- 类内省工具\n    bob = Person('bob')\n    bob.__class__                       # <class 'Person'>\n    bob.__class__.__name__              # 'Person'\n    bob.__dict__                        # {'pay':0, 'name':'bob', 'job':'Manager'}\n    \n#-- 返回1中 数据属性spam是属于类 而不是对象\n    I1 = C1('bob'); I2 = C2('tom')      # 此时I1和I2的spam都为42 但是都是返回的C1的spam属性\n    C1.spam = 24                        # 此时I1和I2的spam都为24\n    I1.spam = 3                         # 此时I1新增自有属性spam 值为3 I2和C1的spam还都为24\n    \n#-- 类方法调用的两种方式\n    instance.method(arg...)\n    class.method(instance, arg...)\n    \n#-- 抽象超类的实现方法\n    # (1)某个函数中调用未定义的函数 子类中定义该函数\n        def delegate(self):\n            self.action()               # 本类中不定义action函数 所以使用delegate函数时就会出错\n    # (2)定义action函数 但是返回异常\n        def action(self):\n            raise NotImplementedError(\"action must be defined\")\n    # (3)上述的两种方法还都可以定义实例对象 实际上可以利用@装饰器语法生成不能定义的抽象超类\n        from abc import ABCMeta, abstractmethod\n        class Super(metaclass = ABCMeta):\n            @abstractmethod\n            def action(self): pass\n        x = Super()                     # 返回 TypeError: Can't instantiate abstract class Super with abstract methods action\n    \n#-- # OOP和继承: \"is-a\"的关系\n    class A(B):\n        pass\n    a = A()\n    isinstance(a, B)                    # 返回True, A是B的子类 a也是B的一种\n    # OOP和组合: \"has-a\"的关系\n    pass\n    # OOP和委托: \"包装\"对象 在Python中委托通常是以\"__getattr__\"钩子方法实现的, 这个方法会拦截对不存在属性的读取\n    # 包装类(或者称为代理类)可以使用__getattr__把任意读取转发给被包装的对象\n    class wrapper(object):\n        def __init__(self, object):\n            self.wrapped = object\n        def __getattr(self, attrname):\n            print('Trace: ', attrname)\n            return getattr(self.wrapped, attrname)\n    # 注:这里使用getattr(X, N)内置函数以变量名字符串N从包装对象X中取出属性 类似于X.__dict__[N]\n    x = wrapper([1, 2, 3])\n    x.append(4)                         # 返回 \"Trace: append\" [1, 2, 3, 4]\n    x = wrapper({'a':1, 'b':2})\n    list(x.keys())                      # 返回 \"Trace: keys\" ['a', 'b']\n\n#-- 类的伪私有属性:使用__attr\n    class C1(object):\n        def __init__(self, name):\n            self.__name = name          # 此时类的__name属性为伪私有属性 原理 它会自动变成self._C1__name = name\n        def __str__(self):\n            return 'self.name = %s' % self.__name\n    I = C1('tom')\n    print(I)                            # 返回 self.name = tom\n    I.__name = 'jeey'                   # 这里无法访问 __name为伪私有属性\n    I._C1__name = 'jeey'                # 这里可以修改成功 self.name = jeey\n    \n#-- 类方法是对象:无绑定类方法对象 / 绑定实例方法对象\n    class Spam(object):\n        def doit(self, message):\n            print(message)\n        def selfless(message)\n            print(message)\n    obj = Spam()\n    x = obj.doit                        # 类的绑定方法对象 实例 + 函数\n    x('hello world')\n    x = Spam.doit                       # 类的无绑定方法对象 类名 + 函数\n    x(obj, 'hello world')\n    x = Spam.selfless                   # 类的无绑定方法函数 在3.0之前无效\n    x('hello world')\n\n#-- 获取对象信息: 属性和方法\n    a = MyObject()\n    dir(a)                              # 使用dir函数\n    hasattr(a, 'x')                     # 测试是否有x属性或方法 即a.x是否已经存在\n    setattr(a, 'y', 19)                 # 设置属性或方法 等同于a.y = 19\n    getattr(a, 'z', 0)                  # 获取属性或方法 如果属性不存在 则返回默认值0\n    #这里有个小技巧，setattr可以设置一个不能访问到的属性，即只能用getattr获取\n    setattr(a, \"can't touch\", 100)      # 这里的属性名带有空格，不能直接访问\n    getattr(a, \"can't touch\", 0)        # 但是可以用getattr获取\n\n#-- 为类动态绑定属性或方法: MethodType方法\n    # 一般创建了一个class的实例后, 可以给该实例绑定任何属性和方法, 这就是动态语言的灵活性\n    class Student(object):\n        pass\n    s = Student()\n    s.name = 'Michael'                  # 动态给实例绑定一个属性\n    def set_age(self, age):             # 定义一个函数作为实例方法\n        self.age = age\n    from types import MethodType\n    s.set_age = MethodType(set_age, s)  # 给实例绑定一个方法 类的其他实例不受此影响\n    s.set_age(25)                       # 调用实例方法\n    Student.set_age = MethodType(set_age, Student)    # 为类绑定一个方法 类的所有实例都拥有该方法\n\n    \n\"\"\"类的高级话题----类的高级话题----类的高级话题----类的高级话题----类的高级话题----类的高级话题----类的高级话题----类的高级话题----类的高级话题----类的高级话题\"\"\"\n    \n#-- 多重继承: \"混合类\", 搜索方式\"从下到上 从左到右 广度优先\"\n    class A(B, C):\n        pass\n\n#-- 类的继承和子类的初始化\n    # 1.子类定义了__init__方法时，若未显示调用基类__init__方法，python不会帮你调用。\n    # 2.子类未定义__init__方法时，python会自动帮你调用首个基类的__init__方法，注意是首个。\n    # 3.子类显示调用基类的初始化函数：\n    class FooParent(object):\n        def __init__(self, a):\n            self.parent = 'I\\'m the Parent.'\n            print('Parent:a=' + str(a))\n        def bar(self, message):\n            print(message + ' from Parent')\n    class FooChild(FooParent):\n        def __init__(self, a):\n            FooParent.__init__(self, a)\n            print('Child:a=' + str(a))\n        def bar(self, message):\n            FooParent.bar(self, message)\n            print(message + ' from Child')\n    fooChild = FooChild(10)\n    fooChild.bar('HelloWorld')\n    \n#-- #实例方法 / 静态方法 / 类方法\n    class Methods(object):\n        def imeth(self, x): print(self, x)      # 实例方法：传入的是实例和数据，操作的是实例的属性\n        def smeth(x): print(x)                  # 静态方法：只传入数据 不传入实例，操作的是类的属性而不是实例的属性\n        def cmeth(cls, x): print(cls, x)        # 类方法：传入的是类对象和数据\n        smeth = staticmethod(smeth)             # 调用内置函数，也可以使用@staticmethod\n        cmeth = classmethod(cmeth)              # 调用内置函数，也可以使用@classmethod\n    obj = Methods()\n    obj.imeth(1)                                # 实例方法调用 <__main__.Methods object...> 1\n    Methods.imeth(obj, 2)                       # <__main__.Methods object...> 2\n    Methods.smeth(3)                            # 静态方法调用 3\n    obj.smeth(4)                                # 这里可以使用实例进行调用\n    Methods.cmeth(5)                            # 类方法调用 <class '__main__.Methods'> 5\n    obj.cmeth(6)                                # <class '__main__.Methods'> 6\n    \n#-- 函数装饰器:是它后边的函数的运行时的声明 由@符号以及后边紧跟的\"元函数\"(metafunction)组成\n        @staticmethod\n        def smeth(x): print(x)\n    # 等同于:\n        def smeth(x): print(x)\n        smeth = staticmethod(smeth)\n    # 同理\n        @classmethod\n        def cmeth(cls, x): print(x)\n    # 等同于\n        def cmeth(cls, x): print(x)\n        cmeth = classmethod(cmeth)\n    \n#-- 类修饰器:是它后边的类的运行时的声明 由@符号以及后边紧跟的\"元函数\"(metafunction)组成\n        def decorator(aClass):.....\n        @decorator\n        class C(object):....\n    # 等同于:\n        class C(object):....\n        C = decorator(C)\n\n#-- 限制class属性: __slots__属性\n    class Student(object):\n        __slots__ = ('name', 'age')             # 限制Student及其实例只能拥有name和age属性\n    # __slots__属性只对当前类起作用, 对其子类不起作用\n    # __slots__属性能够节省内存\n    # __slots__属性可以为列表list，或者元组tuple\n    \n#-- 类属性高级话题: @property\n    # 假设定义了一个类:C，该类必须继承自object类，有一私有变量_x\n    class C(object):\n        def __init__(self):\n            self.__x = None\n    # 第一种使用属性的方法\n        def getx(self):\n            return self.__x\n        def setx(self, value):\n            self.__x = value\n        def delx(self):\n            del self.__x\n        x = property(getx, setx, delx, '')\n    # property函数原型为property(fget=None,fset=None,fdel=None,doc=None)\n    # 使用\n    c = C()\n    c.x = 100                         # 自动调用setx方法\n    y = c.x                           # 自动调用getx方法\n    del c.x                           # 自动调用delx方法\n    # 第二种方法使用属性的方法\n        @property\n        def x(self):\n            return self.__x\n        @x.setter\n        def x(self, value):\n           self.__x = value\n        @x.deleter\n        def x(self):\n           del self.__x\n    # 使用\n    c = C()\n    c.x = 100                         # 自动调用setter方法\n    y = c.x                           # 自动调用x方法\n    del c.x                           # 自动调用deleter方法\n    \n#-- 定制类: 重写类的方法\n    # (1)__str__方法、__repr__方法: 定制类的输出字符串\n    # (2)__iter__方法、next方法: 定制类的可迭代性\n    class Fib(object):\n        def __init__(self):\n            self.a, self.b = 0, 1     # 初始化两个计数器a，b\n        def __iter__(self):\n            return self               # 实例本身就是迭代对象，故返回自己\n        def next(self):\n            self.a, self.b = self.b, self.a + self.b\n            if self.a > 100000:       # 退出循环的条件\n                raise StopIteration()\n            return self.a             # 返回下一个值\n    for n in Fib():\n        print(n)                      # 使用\n    # (3)__getitem__方法、__setitem__方法: 定制类的下标操作[] 或者切片操作slice\n    class Indexer(object):\n        def __init__(self):\n            self.data = {}\n        def __getitem__(self, n):             # 定义getitem方法\n            print('getitem:', n)                \n            return self.data[n]\n        def __setitem__(self, key, value):    # 定义setitem方法\n            print('setitem:key = {0}, value = {1}'.format(key, value))\n            self.data[key] = value\n    test = Indexer()\n    test[0] = 1;   test[3] = '3'              # 调用setitem方法\n    print(test[0])                            # 调用getitem方法\n    # (4)__getattr__方法: 定制类的属性操作\n    class Student(object):\n        def __getattr__(self, attr):          # 定义当获取类的属性时的返回值\n            if attr=='age':\n                return 25                     # 当获取age属性时返回25\n        raise AttributeError('object has no attribute: %s' % attr)\n        # 注意: 只有当属性不存在时 才会调用该方法 且该方法默认返回None 需要在函数最后引发异常\n    s = Student()\n    s.age                                     # s中age属性不存在 故调用__getattr__方法 返回25\n    # (5)__call__方法: 定制类的'可调用'性\n    class Student(object):\n        def __call__(self):                   # 也可以带参数\n            print('Calling......')\n    s = Student()\n    s()                                       # s变成了可调用的 也可以带参数\n    callable(s)                               # 测试s的可调用性 返回True\n    #    (6)__len__方法：求类的长度\n    def __len__(self):\n        return len(self.data)\n    \n#-- 动态创建类type()\n    # 一般创建类 需要在代码中提前定义\n        class Hello(object):\n            def hello(self, name='world'):\n                print('Hello, %s.' % name)\n        h = Hello()\n        h.hello()                             # Hello, world\n        type(Hello)                           # Hello是一个type类型 返回<class 'type'>\n        type(h)                               # h是一个Hello类型 返回<class 'Hello'>\n    # 动态类型语言中 类可以动态创建 type函数可用于创建新类型\n        def fn(self, name='world'):           # 先定义函数\n            print('Hello, %s.' % name)\n        Hello = type('Hello', (object,), dict(hello=fn))    # 创建Hello类 type原型: type(name, bases, dict)\n        h = Hello()                           # 此时的h和上边的h一致\n\n\n\"\"\"异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关\"\"\"\n    \n#-- #捕获异常: \n        try:\n        except:                               # 捕获所有的异常 等同于except Exception:\n        except name:                          # 捕获指定的异常\n        except name, value:                   # 捕获指定的异常和额外的数据(实例)\n        except (name1, name2):\n        except (name1, name2), value:\n        except name4 as X:\n        else:                                 # 如果没有发生异常\n        finally:                              # 总会执行的部分\n    # 引发异常: raise子句(raise IndexError)\n        raise <instance>                      # raise instance of a class, raise IndexError()\n        raise <class>                         # make and raise instance of a class, raise IndexError\n        raise                                 # reraise the most recent exception\n\n#-- Python3.x中的异常链: raise exception from otherException\n    except Exception as X:\n        raise IndexError('Bad') from X\n        \n#-- assert子句: assert <test>, <data>\n    assert x < 0, 'x must be negative'\n    \n#-- with/as环境管理器:作为常见的try/finally用法模式的替代方案\n    with expression [as variable], expression [as variable]:\n    # 例子:\n        with open('test.txt') as myfile:\n            for line in myfile: print(line)\n    # 等同于:\n        myfile = open('test.txt')\n        try:\n            for line in myfile: print(line)\n        finally:\n            myfile.close()\n\n#-- 用户自定义异常: class Bad(Exception):.....\n    \"\"\"\n    Exception超类 / except基类即可捕获到其所有子类\n    Exception超类有默认的打印消息和状态 当然也可以定制打印显示:\n    \"\"\"\n    class MyBad(Exception):\n        def __str__(self):\n            return '定制的打印消息'\n    try:\n        MyBad()\n    except MyBad as x:\n        print(x)\n    \n#-- 用户定制异常数据\n    class FormatError(Exception):\n        def __init__(self, line ,file):\n            self.line = line\n            self.file = file\n    try:\n        raise FormatError(42, 'test.py')\n    except FormatError as X:\n        print('Error at ', X.file, X.line)\n    # 用户定制异常行为(方法):以记录日志为例\n    class FormatError(Exception):\n        logfile = 'formaterror.txt'\n        def __init__(self, line ,file):\n            self.line = line\n            self.file = file\n        def logger(self):\n            open(self.logfile, 'a').write('Error at ', self.file, self.line)\n    try:\n        raise FormatError(42, 'test.py')\n    except FormatError as X:\n        X.logger()\n\n#-- 关于sys.exc_info:允许一个异常处理器获取对最近引发的异常的访问\n    try:\n        ......\n    except:\n        # 此时sys.exc_info()返回一个元组(type, value, traceback)\n        # type:正在处理的异常的异常类型\n        # value:引发的异常的实例\n        # traceback:堆栈信息\n        \n#-- 异常层次\n    BaseException\n    +-- SystemExit\n    +-- KeyboardInterrupt\n    +-- GeneratorExit\n    +-- Exception\n        +-- StopIteration\n        +-- ArithmeticError\n        +-- AssertionError\n        +-- AttributeError\n        +-- BufferError\n        +-- EOFError\n        +-- ImportError\n        +-- LookupError\n        +-- MemoryError\n        +-- NameError\n        +-- OSError\n        +-- ReferenceError\n        +-- RuntimeError\n        +-- SyntaxError\n        +-- SystemError\n        +-- TypeError\n        +-- ValueError\n        +-- Warning\n\n    \n\"\"\"Unicode和字节字符串---Unicode和字节字符串----Unicode和字节字符串----Unicode和字节字符串----Unicode和字节字符串----Unicode和字节字符串----Unicode和字节字符串\"\"\"\n\n#-- Python的字符串类型\n    \"\"\"Python2.x\"\"\"\n    # 1.str表示8位文本和二进制数据\n    # 2.unicode表示宽字符Unicode文本\n    \"\"\"Python3.x\"\"\"\n    # 1.str表示Unicode文本（8位或者更宽）\n    # 2.bytes表示不可变的二进制数据\n    # 3.bytearray是一种可变的bytes类型\n\n#-- 字符编码方法\n    \"\"\"ASCII\"\"\"                   # 一个字节，只包含英文字符，0到127，共128个字符，利用函数可以进行字符和数字的相互转换\n    ord('a')                      # 字符a的ASCII码为97，所以这里返回97\n    chr(97)                       # 和上边的过程相反，返回字符'a'\n    \"\"\"Latin-1\"\"\"                 # 一个字节，包含特殊字符，0到255，共256个字符，相当于对ASCII码的扩展\n    chr(196)                      # 返回一个特殊字符：Ä\n    \"\"\"Unicode\"\"\"                 # 宽字符，一个字符包含多个字节，一般用于亚洲的字符集，比如中文有好几万字\n    \"\"\"UTF-8\"\"\"                   # 可变字节数，小于128的字符表示为单个字节，128到0X7FF之间的代码转换为两个字节，0X7FF以上的代码转换为3或4个字节\n    # 注意：可以看出来，ASCII码是Latin-1和UTF-8的一个子集\n    # 注意：utf-8是unicode的一种实现方式，unicode、gbk、gb2312是编码字符集\n    \n#-- 查看Python中的字符串编码名称，查看系统的编码\n    import encodings\n    help(encoding)\n    import sys\n    sys.platform                  # 'win64'\n    sys.getdefaultencoding()      # 'utf-8'\n    sys.getdefaultencoding()      # 返回当前系统平台的编码类型\n    sys.getsizeof(object)         # 返回object占有的bytes的大小\n    \n#-- 源文件字符集编码声明: 添加注释来指定想要的编码形式 从而改变默认值 注释必须出现在脚本的第一行或者第二行\n    \"\"\"说明：其实这里只会检查#和coding:utf-8，其余的字符都是为了美观加上的\"\"\"\n    # _*_ coding: utf-8 _*_\n    # coding = utf-8\n    \n#-- #编码: 字符串 --> 原始字节       #解码: 原始字节 --> 字符串\n\n#-- Python3.x中的字符串应用\n    s = '...'                     # 构建一个str对象，不可变对象\n    b = b'...'                    # 构建一个bytes对象，不可变对象\n    s[0], b[0]                    # 返回('.', 113)\n    s[1:], b[1:]                  # 返回('..', b'..')\n    B = B\"\"\"\n        xxxx\n        yyyy\n        \"\"\"\n    # B = b'\\nxxxx\\nyyyy\\n'\n    # 编码，将str字符串转化为其raw bytes形式：\n        str.encode(encoding = 'utf-8', errors = 'strict')\n        bytes(str, encoding)\n    # 编码例子：\n        S = 'egg'\n        S.encode()                    # b'egg'\n        bytes(S, encoding = 'ascii')  # b'egg'\n    # 解码，将raw bytes字符串转化为str形式：\n        bytes.decode(encoding = 'utf-8', errors = 'strict')\n        str(bytes_or_buffer[, encoding[, errors]])\n    # 解码例子：\n        B = b'spam'\n        B.decode()                # 'spam'\n        str(B)                    # \"b'spam'\"，不带编码的str调用，结果为打印该bytes对象\n        str(B, encoding = 'ascii')# 'spam'，带编码的str调用，结果为转化该bytes对象\n    \n#-- Python2.x的编码问题\n    u = u'汉'\n    print repr(u)                 # u'\\xba\\xba'\n    s = u.encode('UTF-8')\n    print repr(s)                 # '\\xc2\\xba\\xc2\\xba'\n    u2 = s.decode('UTF-8')\n    print repr(u2)                # u'\\xba\\xba'\n    # 对unicode进行解码是错误的\n    s2 = u.decode('UTF-8')        # UnicodeEncodeError: 'ascii' codec can't encode characters in position 0-1: ordinal not in range(128)\n    # 同样，对str进行编码也是错误的\n    u2 = s.encode('UTF-8')        # UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 0: ordinal not in range(128)\n\n#-- bytes对象\n    B = b'abc'\n    B = bytes('abc', 'ascii')\n    B = bytes([97, 98, 99])\n    B = 'abc'.encode()\n    # bytes对象的方法调用基本和str类型一致 但:B[0]返回的是ASCII码值97, 而不是b'a'\n    \n#-- #文本文件: 根据Unicode编码来解释文件内容，要么是平台的默认编码，要么是指定的编码类型\n    # 二进制文件：表示字节值的整数的一个序列 open('bin.txt', 'rb')\n    \n#-- Unicode文件\n    s = 'A\\xc4B\\xe8C'             # s = 'A?BèC'  len(s) = 5\n    #手动编码\n        l = s.encode('latin-1')   # l = b'A\\xc4B\\xe8C'  len(l) = 5\n        u = s.encode('utf-8')     # u = b'A\\xc3\\x84B\\xc3\\xa8C'  len(u) = 7\n    #文件输出编码\n        open('latindata', 'w', encoding = 'latin-1').write(s)\n        l = open('latindata', 'rb').read()                        # l = b'A\\xc4B\\xe8C'  len(l) = 5\n        open('uft8data', 'w', encoding = 'utf-8').write(s)\n        u = open('uft8data', 'rb').read()                         # u = b'A\\xc3\\x84B\\xc3\\xa8C'  len(u) = 7\n    #文件输入编码\n        s = open('latindata', 'r', encoding = 'latin-1').read()   # s = 'A?BèC'  len(s) = 5\n        s = open('latindata', 'rb').read().decode('latin-1')      # s = 'A?BèC'  len(s) = 5\n        s = open('utf8data', 'r', encoding = 'utf-8').read()      # s = 'A?BèC'  len(s) = 5\n        s = open('utf8data', 'rb').read().decode('utf-8')         # s = 'A?BèC'  len(s) = 5\n        \n\n\"\"\"其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他\"\"\"\n\n#-- Python实现任意深度的赋值 例如a[0] = 'value1'; a[1][2] = 'value2'; a[3][4][5] = 'value3'\n    class MyDict(dict):\n        def __setitem__(self, key, value):                 # 该函数不做任何改动 这里只是为了输出\n            print('setitem:', key, value, self)\n            super().__setitem__(key, value)\n        def __getitem__(self, item):                       # 主要技巧在该函数\n            print('getitem:', item, self)                  # 输出信息\n            # 基本思路: a[1][2]赋值时 需要先取出a[1] 然后给a[1]的[2]赋值\n            if item not in self:                           # 如果a[1]不存在 则需要新建一个dict 并使得a[1] = dict\n                temp = MyDict()                            # 新建的dict: temp\n                super().__setitem__(item, temp)            # 赋值a[1] = temp\n                return temp                                # 返回temp 使得temp[2] = value有效\n            return super().__getitem__(item)               # 如果a[1]存在 则直接返回a[1]\n    # 例子:\n        test = MyDict()\n        test[0] = 'test'\n        print(test[0])\n        test[1][2] = 'test1'\n        print(test[1][2])\n        test[1][3] = 'test2'\n        print(test[1][3])\n\n#-- Python中的多维数组\n    lists = [0] * 3                                        # 扩展list，结果为[0, 0, 0]\n    lists = [[]] * 3                                       # 多维数组，结果为[[], [], []]，但有问题，往下看\n    lists[0].append(3)                                     # 期望看到的结果[[3], [], []]，实际结果[[3], [3], [3]]，原因：list*n操作，是浅拷贝，如何避免？往下看\n    lists = [[] for i in range(3)]                         # 多维数组，结果为[[], [], []]\n    lists[0].append(3)                                     # 结果为[[3], [], []]\n    lists[1].append(6)                                     # 结果为[[3], [6], []]\n    lists[2].append(9)                                     # 结果为[[3], [6], [9]]\n    lists = [[[] for j in range(4)] for i in range(3)]     # 3行4列，且每一个元素为[]\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/python的各种pass.md",
    "content": "#Python的各种Pass\n\n感觉最近对于pass by reference 和 pass by value又有了一点/一些认识\n\n\n1. python不允许程序员选择采用传值还是传引用。Python参数传递采用的肯定是“传对象引用”的方式。实际上，这种方式相当于传值和传引用的一种综合。如果函数收到的是一个可变对象（比如字典或者列表）的引用，就能修改对象的原始值——相当于通过“传引用”来传递对象。如果函数收到的是一个不可变对象（比如数字、字符或者元组）的引用，就不能直接修改原始对象——相当于通过“传值'来传递对象。\n2. 当人们复制列表或字典时，就复制了对象列表的引用同，如果改变引用的值，则修改了原始的参数。\n3. 为了简化内存管理，Python通过引用计数机制实现自动垃圾回收功能，Python中的每个对象都有一个引用计数，用来计数该对象在不同场所分别被引用了多少次。每当引用一次Python对象，相应的引用计数就增1，每当消毁一次Python对象，则相应的引用就减1，只有当引用计数为零时，才真正从内存中删除Python对象。\n\n\n##### Linked List的例子\n\n\n\n```\nclass ListNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n        \nnode1 = ListNode(1)\nnode2 = ListNode(2)\nnode3 = ListNode(3)\nnode4 = ListNode(4)\nnode5 = ListNode(5)\n\nnode1.next = node2\nnode2.next = node3\nnode3.next = node4\nnode4.next = node5\n  \n```\n\n\n\n来改变head\n\n```\ndef testWithPointers1(head):\n    head.next = None   \n```\n\n\n\n运行 testWithPointers1(node1)\n\n然后node1.next 为None了  \n\n// 可以理解，因为传进去的是head这个可变对象。\n\n\n\n```\ndef testWithPointers2(head):\n    cur = head\n    cur.next = None\n```\n\n\n\n运行 testWithPointers2(node1)\n// node1.next 同样为None了   \n\nPython的object，list都是pass by reference，所以是改变的\n\n看另外一个例子：\n\n```\ndef printLinkedList(head):\n\twhile head:\n\t\tprint(head)\n\t\thead = head.next\n```\n\n\n输出\n\n```\n  printLinkedList(head)\n\n<__main__.ListNode object at 0x1044c0e10>\n\n1\n\n<__main__.ListNode object at 0x1044c0fd0>\n\n2\n\n<__main__.ListNode object at 0x1044c0c88>\n\n3\n\n<__main__.ListNode object at 0x1044c0be0>\n\n4\n\n<__main__.ListNode object at 0x1044c0780>\n\n5\n\nhead\n\nOut[39]: <__main__.ListNode at 0x1044c0e10>\n\n```\n\n其实这里的head为什么没有改变有点疑惑\n\n\n\n##### String看一下\n\n\n    a  = \"abc\"\n    \n    def changeA(s):\n    \ts = \"\"\n    changeA(a)\n\n\na 并不会改变，依旧为'abc'\n\n "
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/slide_windows_template.md",
    "content": "能用此模板解决的题目目前有如下：\n[leetcode 003](https://github.com/Lisanaaa/thinking_in_lc/blob/master/003._longest_substring_without_repeating_characters.md), \n[leetcode 030](https://github.com/Lisanaaa/thinking_in_lc/edit/master/30._Substring_with_Concatenation_of_All_Words.md), \n[leetcode 076](https://github.com/Lisanaaa/thinking_in_lc/blob/master/076._Minimum_Window_Substring.md), \n[leetcode 159](https://github.com/Lisanaaa/thinking_in_lc/blob/master/159._Longest_Substring_with_At_Most_Two_Distinct_Characters.md), \n[leetcode 438](https://github.com/Lisanaaa/thinking_in_lc/blob/master/438._Find_All_Anagrams_in_a_String.md)\n\n\n\n带注释python版本\n```python\nclass Solution(object):\n    def slideWindowTemplateByLisanaaa(self, s, t):\n        \"\"\"\n        :type s: str\n        :type t: str\n        :rtype: 具体题目具体分析\n        \"\"\"\n        # init a collection or int value to save the result according the question.\n        res = []\n        if len(t) > len(s):\n            return res\n        \n        # create a hashmap to save the Characters of the target substring.\n        # (K, V) = (Character, Frequence of the Characters)\n        maps = collections.Counter(t)\n        \n        # maintain a counter to check whether match the target string.\n        # must be the map size, NOT the string size because the char may be duplicate.\n        counter = len(maps.keys()) \n        \n        # Two Pointers: begin - left pointer of the window; end - right pointer of the window\n        begin, end = 0, 0\n        \n        # the length of the substring which match the target string.\n        length = sys.maxint \n        \n        # loop at the begining of the source string\n        while end < len(s):\n            if s[end] in maps:\n                maps[s[end]] -= 1 # plus or minus one\n                if maps[s[end]] == 0:\n                    counter -= 1 # modify the counter according the requirement(different condition).\n            end += 1\n            \n            # increase begin pointer to make it invalid/valid again\n            while counter == 0: # counter condition. different question may have different condition\n                if s[begin] in maps:\n                    maps[s[begin]] += 1 # plus or minus one\n                    if maps[s[begin]] > 0:\n                        counter += 1 # modify the counter according the requirement(different condition).\n                begin += 1\n                \n                '''\n                type your code here according to the question\n                1. save / update(min/max) the result if find a target\n                2. result: collections or int value\n                '''  \n        return res\n```\n\n无注释python版本：\n```python\nclass Solution(object):\n    def slideWindowTemplateByLisanaaa(self, s, t):\n        res = []\n        if len(t) > len(s):\n            return res\n        maps = collections.Counter(t)\n        counter = len(maps.keys()) \n        begin, end = 0, 0\n        length = sys.maxint \n        while end < len(s):\n            if s[end] in maps:\n                maps[s[end]] -= 1 \n                if maps[s[end]] == 0:\n                    counter -= 1 \n            end += 1\n            while counter == 0:\n                if s[begin] in maps:\n                    maps[s[begin]] += 1 \n                    if maps[s[begin]] > 0:\n                        counter += 1 \n                begin += 1\n                \n                '''\n                1. save / update(min/max) the result if find a target\n                2. result: collections or int value\n                '''  \n        return res\n```\n带注释java版本\n```java\npublic class Solution {\n    public List<Integer> slidingWindowTemplateByHarryChaoyangHe(String s, String t) {\n        //init a collection or int value to save the result according the question.\n        List<Integer> result = new LinkedList<>();\n        if(t.length()> s.length()) return result;\n        \n        //create a hashmap to save the Characters of the target substring.\n        //(K, V) = (Character, Frequence of the Characters)\n        Map<Character, Integer> map = new HashMap<>();\n        for(char c : t.toCharArray()){\n            map.put(c, map.getOrDefault(c, 0) + 1);\n        }\n        //maintain a counter to check whether match the target string.\n        int counter = map.size();//must be the map size, NOT the string size because the char may be duplicate.\n        \n        //Two Pointers: begin - left pointer of the window; end - right pointer of the window\n        int begin = 0, end = 0;\n        \n        //the length of the substring which match the target string.\n        int len = Integer.MAX_VALUE; \n        \n        //loop at the begining of the source string\n        while(end < s.length()){\n            \n            char c = s.charAt(end);//get a character\n            \n            if( map.containsKey(c) ){\n                map.put(c, map.get(c)-1);// plus or minus one\n                if(map.get(c) == 0) counter--;//modify the counter according the requirement(different condition).\n            }\n            end++;\n            \n            //increase begin pointer to make it invalid/valid again\n            while(counter == 0 /* counter condition. different question may have different condition */){\n                \n                char tempc = s.charAt(begin);//***be careful here: choose the char at begin pointer, NOT the end pointer\n                if(map.containsKey(tempc)){\n                    map.put(tempc, map.get(tempc) + 1);//plus or minus one\n                    if(map.get(tempc) > 0) counter++;//modify the counter according the requirement(different condition).\n                }\n                \n                /* save / update(min/max) the result if find a target*/\n                // result collections or result int value\n                \n                begin++;\n            }\n        }\n        return result;\n    }\n}\n```\n\n无注释java版本：\n```java\npublic class Solution {\n    public List<Integer> slidingWindowTemplateByHarryChaoyangHe(String s, String t) {\n        List<Integer> result = new LinkedList<>();\n        if(t.length()> s.length()) return result;\n        Map<Character, Integer> map = new HashMap<>();\n        for(char c : t.toCharArray()){\n            map.put(c, map.getOrDefault(c, 0) + 1);\n        }\n        int counter = map.size();\n        int begin = 0, end = 0;\n        int len = Integer.MAX_VALUE; \n        while(end < s.length()){\n            char c = s.charAt(end);\n            if( map.containsKey(c) ){\n                map.put(c, map.get(c)-1);\n                if(map.get(c) == 0) counter--;\n            }\n            end++;\n            while(counter == 0){\n                char tempc = s.charAt(begin);\n                if(map.containsKey(tempc)){\n                    map.put(tempc, map.get(tempc) + 1);\n                    if(map.get(tempc) > 0) counter++;\n                }\n                \n                /* \n                save / update(min/max) the result if find a target\n                result collections or result int value\n                */\n                \n                begin++;\n            }\n        }\n        return result;\n    }\n}\n```\n\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/union_find.md",
    "content": "## 并查集（参考leetcode323题）\n\n### 应用场景：\n\n动态联通性\n\n- 网络连接判断：\n如果每个pair中的两个整数用来表示这两个节点是需要连通的，那么为所有的pairs建立了动态连通图后，就能够尽可能少的减少布线的需要，\n因为已经连通的两个节点会被直接忽略掉。例如[1,2]代表节点1和节点2是联通的，如果再次出现[2,1]我们就不需要再连接他们了，因为已经连接过一次了。\n\n- 变量名等同性(类似于指针的概念)：\n在程序中，可以声明多个引用来指向同一对象，这个时候就可以通过为程序中声明的引用和实际对象建立动态连通图来判断哪些引用实际上是指向同一对象。\n\n### 对问题建模：\n\n在对问题进行建模的时候，我们应该尽量想清楚需要解决的问题是什么。因为模型中选择的数据结构和算法显然会根据问题的不同而不同，\n就动态连通性这个场景而言，我们需要解决的问题可能是：\n\n1. 给出两个节点，判断它们是否连通，如果连通，不需要给出具体的路径\n2. 给出两个节点，判断它们是否连通，如果连通，需要给出具体的路径\n\n就上面两种问题而言，虽然只有是否能够给出具体路径的区别，但是这个区别导致了选择算法的不同\n\n### 建模思路：\n\n最简单而直观的假设是，对于连通的所有节点，我们可以认为它们属于一个组，因此不连通的节点必然就属于不同的组。\n随着Pair的输入，我们需要首先判断输入的两个节点是否连通。如何判断呢？按照上面的假设，我们可以通过判断它们属于的组，然后看看这两个组是否相同。\n如果相同，那么这两个节点连通，反之不连通。为简单起见，我们将所有的节点以整数表示，即对N个节点使用0到N-1的整数表示。\n而在处理输入的Pair之前，每个节点必然都是孤立的，即他们分属于不同的组，可以使用数组来表示这一层关系。\n数组的index是节点的整数表示，而相应的值就是该节点的组号了。该数组可以初始化为：```uf = [i for i in range(n)]```\n\n初始化完毕之后，对该动态连通图有几种可能的操作：\n\n1. 查询节点属于的组\n\n数组对应位置的值即为组号\n\n2. 判断两个节点是否属于同一个组\n\n分别得到两个节点的组号，然后判断组号是否相等\n\n3. 连接两个节点，使之属于同一个组\n\n分别得到两个节点的组号，组号相同时操作结束，不同时，将其中的一个节点的组号换成另一个节点的组号\n\n4. 获取组的数目\n\n初始化为节点的数目，然后每次成功连接两个节点之后，递减1\n\n### API：\n\n我们可以设置对应的API\n```python\ndef uf(n)   # 初始化uf数组和组数目\ndef union(x, y) # 连接两个节点\ndef find(x) # 判断节点所属于的组\ndef connected(x, y) # 判断两个节点是否联通\ndef count(x) # 返回所有组的数目\n```\n\n注意其中使用整数来表示节点，如果需要使用其他的数据类型表示节点，比如使用字符串，那么可以用哈希表来进行映射，即将String映射成这里需要的Integer类型。\n\n分析以上的API，方法connected和union都依赖于find，connected对两个参数调用两次find方法，而union在真正执行union之前也需要判断是否连通，\n这又是两次调用find方法。因此我们需要把find方法的实现设计的尽可能的高效。所以就有了下面的Quick-Find实现。\n\n### Quick-Find实现\n\n```\nclass Solution(object):\n    uf = []    # access to component id (site indexed)\n    count = 0  # number of components\n\n    def uf(n):  # 初始化uf数组和组数目\n        self.count = n\n        self.uf = [i for i in range(n)]    \n        \n    def find(self, x):  # 判断节点所属于的组\n        return uf[x]\n\n    def union(self, x, y):  # 连接两个节点\n        x_root = find(x)\n        y_root = find(y)\n        if x_root == y_root:\n            return\n        for i in range(len(self.uf)):\n            if uf[i] == x_root:\n                uf[i] = y_root\n        count -= 1\n    \n    def connected(self, x, y):  # 判断两个节点是否联通\n        return find(x) == find(y)\n    \n    def count(x):  # 返回所有组的数目\n        return count               \n```\n\n上述代码的find方法十分高效，因为仅仅需要一次数组读取操作就能够找到该节点的组号。\n但是问题随之而来，对于需要添加新路径的情况，就涉及到对于组号的修改，因为并不能确定哪些节点的组号需要被修改，\n因此就必须对整个数组进行遍历，找到需要修改的节点，逐一修改，这一下每次添加新路径带来的复杂度就是线性关系了，\n如果要添加的新路径的数量是M，节点数量是N，那么最后的时间复杂度就是MN，显然是一个平方阶的复杂度，对于大规模的数据而言，平方阶的算法是存在问题的，\n这种情况下，每次添加新路径就是“牵一发而动全身”，想要解决这个问题，关键就是要提高union方法的效率，让它不再需要遍历整个数组。\n\n\n### Quick-Union 算法：\n\n考虑一下，为什么以上的解法会造成“牵一发而动全身”？因为每个节点所属的组号都是单独记录，各自为政的，没有将它们以更好的方式组织起来，\n当涉及到修改的时候，除了逐一通知、修改，别无他法。所以现在的问题就变成了，如何将节点以更好的方式组织起来，组织的方式有很多种，\n但是最直观的还是将组号相同的节点组织在一起，想想所学的数据结构，什么样子的数据结构能够将一些节点给组织起来？\n常见的就是链表，图，树，什么的了。但是哪种结构对于查找和修改的效率最高？毫无疑问是树，因此考虑如何将节点和组的关系以树的形式表现出来。\n\n如果不改变底层数据结构，即不改变使用数组的表示方法的话。可以采用parent-link的方式将节点组织起来，\n举例而言，uf[p]的值就是p节点的父节点的序号，如果p是树根的话，uf[p]的值就是p，因此最后经过若干次查找，一个节点总是能够找到它的根节点，\n即满足uf[root] = root的节点也就是组的根节点了，然后就可以使用根节点的序号来表示组号。所以在处理一个pair的时候，\n将首先找到pair中每一个节点的组号(即它们所在树的根节点的序号)，如果属于不同的组的话，就将其中一个根节点的父节点设置为另外一个根节点，\n相当于将一颗独立的树编程另一颗独立的树的子树。直观的过程如下图所示。但是这个时候又引入了问题。\n\n树这种数据结构容易出现极端情况，因为在建树的过程中，树的最终形态严重依赖于输入数据本身的性质，比如数据是否排序，是否随机分布等等。\n比如在输入数据是有序的情况下，构造的BST会退化成一个链表。在我们这个问题中，也是会出现的极端情况的。\n\n```\nclass Solution(object):\n    uf = []    # access to component id (site indexed)\n    count = 0  # number of components\n\n    def uf(n):  # 初始化uf数组和组数目\n        self.count = n\n        self.uf = [i for i in range(n)]    \n        \n    def find(self, x):  # 判断节点所属于的组\n    # if uf[x] != x:      ## 这种方式也可以，但是递归次数多了容易出问题\n    # \tuf[x] = find(uf[x])\n        while x != uf[x]:\n            x = uf[x]\n        return uf[x]\n\n    def union(self, x, y):  # 连接两个节点\n        x_root = find(x)\n        y_root = find(y)\n        uf[x_root] = y_root\n        count -= 1\n    \n    def connected(self, x, y):  # 判断两个节点是否联通\n        return find(x) == find(y)\n    \n    def count(x):  # 返回所有组的数目\n        return count        \n```\n\n为了克服这个问题，BST可以演变成为红黑树或者AVL树等等。\n\n然而，在我们考虑的这个应用场景中，每对节点之间是不具备可比性的。因此需要想其它的办法。在没有什么思路的时候，多看看相应的代码可能会有一些启发，\n考虑一下Quick-Union算法中的union方法实现：\n\n```\ndef union(self, x, y):  # 连接两个节点\n    x_root = find(x)\n    y_root = find(y)\n    uf[x_root] = y_root\n    count -= 1\n```\n\n上面 id[pRoot] = qRoot 这行代码看上去似乎不太对劲。因为这也属于一种“硬编码”，这样实现是基于一个约定，即p所在的树总是会被作为q所在树的子树，\n从而实现两颗独立的树的融合。那么这样的约定是不是总是合理的呢？显然不是，比如p所在的树的规模比q所在的树的规模大的多时，\np和q结合之后形成的树就是十分不和谐的一头轻一头重的”畸形树“了。\n\n所以我们应该考虑树的大小，然后再来决定到底是调用 uf[x_root] = y_root 或者是 uf[y_root] = x_root\n\n即总是size小的树作为子树和size大的树进行合并。这样就能够尽量的保持整棵树的平衡。\n\n所以现在的问题就变成了：树的大小该如何确定？\n\n我们回到最初的情形，即每个节点最一开始都是属于一个独立的组，通过下面的代码进行初始化：\n```\ntree_size = [1 for i in range(n)]\n```\n\n然后：\n\n```\ndef union(self, x, y):  # 连接两个节点\n    x_root = find(x)\n    y_root = find(y)\n\tif tree_size[x_root] <= tree_size[y_root]:\n    \tuf[x_root] = y_root\n\t\ttree_size[y_root] += tree_size[x_root]\n\telse:\n    \tuf[y_root] = x_root\n\t\ttree_size[x_root] += tree_size[y_root]\n    count -= 1\n```\n\n可以发现，通过tree_size数组决定如何对两棵树进行合并之后，最后得到的树的高度大幅度减小了。这是十分有意义的。\n因为在Quick-Union算法中的任何操作，都不可避免的需要调用find方法，而该方法的执行效率依赖于树的高度。树的高度减小了，\nfind方法的效率就增加了，从而也就增加了整个Quick-Union算法的效率。\n\n上面的论证其实还可以给我们一些启示，即对于Quick-Union算法而言，节点组织的理想情况应该是一颗十分扁平的树，所有的孩子节点应该都在height为1的地方，\n即所有的孩子都直接连接到根节点。这样的组织结构能够保证find操作的最高效率。\n\n那么如何构造这种理想结构呢？\n\n在find方法的执行过程中，不是需要进行一个while循环找到根节点嘛？如果保存所有路过的中间节点到一个数组中，然后在while循环结束之后，\n将这些中间节点的父节点指向根节点，不就行了么？但是这个方法也有问题，因为find操作的频繁性，会造成频繁生成中间节点数组，\n相应的分配销毁的时间自然就上升了。那么有没有更好的方法呢？还是有的，即将节点的父节点指向该节点的爷爷节点，这一点很巧妙，\n十分方便且有效，相当于在寻找根节点的同时，对路径进行了压缩，使整个树结构扁平化。相应的实现如下，实际上只需要添加一行代码：\n\n```\ndef find(self, x):  # 判断节点所属于的组\n\tuf[x] = uf[uf[x]]\n\treturn uf[x]\n```\n综上，我决定以后解决问题的时候用这个模版就行了：\n\n```python\nclass Solution(object):\n    uf = []    # access to component id (site indexed)\n    count = 0  # number of components\n\n    def uf(n):  # 初始化uf数组和组数目\n        self.count = n\n        self.uf = [i for i in range(n)]    \n        \n    def find(self, x):  # 判断节点所属于的组\n        while x != uf[x]:\n\t    uf[x] = uf[uf[x]]\n            x = uf[x]\n        return uf[x]\n\n    def union(self, x, y):  # 连接两个节点\n        x_root = find(x)\n        y_root = find(y)\n        uf[x_root] = y_root\n        count -= 1\n    \n    def connected(self, x, y):  # 判断两个节点是否联通\n        return find(x) == find(y)\n    \n    def count(x):  # 返回所有组的数目\n        return count        \n```\n至此，动态连通性相关的Union-Find算法基本上就介绍完了，从容易想到的Quick-Find到相对复杂但是更加高效的Quick-Union，然后到对Quick-Union的几项改进，\n让我们的算法的效率不断的提高。\n\n这几种算法的时间复杂度如下所示：\n![](https://github.com/apachecn/LeetCode/blob/master/images/union_find8D76B4AFCB73CED67BE37B92B385A55C.jpg)\n\n对大规模数据进行处理，使用平方阶的算法是不合适的，比如简单直观的Quick-Find算法，通过发现问题的更多特点，找到合适的数据结构，然后有针对性的进行改进，\n得到了Quick-Union算法及其多种改进算法，最终使得算法的复杂度降低到了近乎线性复杂度。\n\n如果需要的功能不仅仅是检测两个节点是否连通，还需要在连通时得到具体的路径，那么就需要用到别的算法了，比如DFS或者BFS。\n\n并查集的应用，可以参考另外一篇文章[并查集应用举例](https://blog.csdn.net/dm_vincent/article/details/7769159)\n\n## References\n\n1. [并查集(Union-Find)算法介绍](https://blog.csdn.net/dm_vincent/article/details/7655764)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/二叉树的一些操作.md",
    "content": "### 1. 二叉搜索树（BSTree）的概念\n\n二叉搜索树又被称为二叉排序树，那么它本身也是一棵二叉树，那么满足以下性质的二叉树就是二叉搜索树，如图：\n\n- 若左子树不为空，则左子树上所有节点的值都小于根节点的值；\n- 若它的右子树不为空，则它的右子树上所有节点的值都大于根节点的值；\n- 它的左右子树也要分别是二叉搜索树。\n\n### 2. 二叉搜索树的插入\n\n- 如果插入值已经存在，则不插，return False\n- 如果not root，则返回TreeNode(val)\n- 根据与root.val的比较，在左右子树中进行递归操作\n\n代码中保证插入的值不存在，也是[leetcode第701题](https://github.com/apachecn/LeetCode/blob/master/docs/Leetcode_Solutions/701._Insert_into_a_Binary_Search_Tree.md)中所gurantee的\n```python\n# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution(object):\n    def insertIntoBST(self, root, val):\n        \"\"\"\n        :type root: TreeNode\n        :type val: int\n        :rtype: TreeNode\n        \"\"\"\n        if not root:\n            return TreeNode(val)\n        if val < root.val:\n            root.left = self.insertIntoBST(root.left, val)\n        if val > root.val:\n            root.right = self.insertIntoBST(root.right, val)\n        return root\n```\n\n### 3. 二叉搜索树的搜索\n\n*  搜索节点\n```java\npublic TreeNode search(int key) {\n    TreeNode pNode = root;\n    while (pNode != null) {\n        if (key == pNode.key) {\n            return pNode;\n        } else if (key > pNode.key) {\n            pNode = pNode.rchild;\n        } else if (key < pNode.key) {\n            pNode = pNode.lchild;\n        }\n    }\n    return null;// 如果没有搜索到结果那么就只能返回空值了\n}\n```\n\n* 获取最小节点\n\n```java\npublic TreeNode minElemNode(TreeNode node) throws Exception {\n    if (node == null) {\n        throw new Exception(\"此树为空树！\");\n    }\n    TreeNode pNode = node;\n    while (pNode.lchild != null) {\n        pNode = pNode.lchild;\n    }\n    return pNode;\n}\n```\n\n* 获取最大节点\n\n```java\npublic TreeNode maxElemNode(TreeNode node) throws Exception {\n    if (node == null) {\n        throw new Exception(\"此树为空树！\");\n    }\n    TreeNode pNode = node;\n    while (pNode.rchild != null) {\n        pNode = pNode.rchild;\n    }\n    return pNode;\n}\n```\n\n* 获取给定节点在中序遍历下的后续第一个节点（即找到该节点的右子树中的最左孩子）\n\n```java\npublic TreeNode successor(TreeNode node) throws Exception {\n    if (node == null) {\n        throw new Exception(\"此树为空树！\");\n    }\n    // 分两种情况考虑，此节点是否有右子树\n    // 当这个节点有右子树的情况下，那么右子树的最小关键字节点就是这个节点的后续节点\n    if (node.rchild != null) {\n        return minElemNode(node.rchild);\n    }\n\n    // 当这个节点没有右子树的情况下,即 node.rchild == null\n    // 如果这个节点是它父节点的左子树的话，那么就说明这个父节点就是后续节点了\n    TreeNode parentNode = node.parent;\n    while (parentNode != null && parentNode.rchild == node) {\n        node = parentNode;\n        parentNode = parentNode.parent;\n    }\n    return parentNode;\n}\n```\n\n\n* 获取给定节点在中序遍历下的前趋结点\n\n```java\npublic TreeNode precessor(TreeNode node) throws Exception {\n    // 查找前趋节点也是分两种情况考虑\n    // 如果这个节点存在左子树，那么这个左子树的最大关键字就是这个节点的前趋节点\n    if (node.lchild != null) {\n        return maxElemNode(node.lchild);\n    }\n    // 如果这个节点不存在左子树，那么这个节点的父节点\n    TreeNode parentNode = node.parent;\n    while (parentNode != null && parentNode.lchild == node) {\n        node = parentNode;\n        parentNode = parentNode.lchild;\n    }\n    return parentNode;\n}\n```\n\n\n\n### 4. 二叉搜索树的删除\n\n- 要删除的节点不存在，return False\n- 要删除的节点没有子节点，直接删\n- 要删除的节点只有一个子节点（即只有一个左子节点或者一个右子节点），让被删除节点的父亲节点指向其子节点即可\n- 要删除的节点target有两个子节点（即左右均存在），则首先找到该节点的右子树中的最左孩子（也就是右子树中序遍历的第一个节点，分两种情况），\n然后将两者互换，再删除target即可\n\n```java\n    // 从二叉树当中删除指定的节点\n    public void delete(int key) throws Exception {\n        TreeNode pNode = search(key);\n        if (pNode == null) {\n            throw new Exception(\"此树中不存在要删除的这个节点！\");\n        }\n\n        delete(pNode);\n    }\n    \n    private void delete(TreeNode pNode) throws Exception {\n        // 第一种情况:删除没有子节点的节点\n        if (pNode.lchild == null && pNode.rchild == null) {\n            if (pNode == root) {// 如果是根节点，那么就删除整棵树\n                root = null;\n            } else if (pNode == pNode.parent.lchild) {\n                // 如果这个节点是父节点的左节点，则将父节点的左节点设为空\n                pNode.parent.lchild = null;\n            } else if (pNode == pNode.parent.rchild) {\n                // 如果这个节点是父节点的右节点，则将父节点的右节点设为空\n                pNode.parent.rchild = null;\n            }\n        }\n\n        // 第二种情况： （删除有一个子节点的节点）\n        // 如果要删除的节点只有右节点\n        if (pNode.lchild == null && pNode.rchild != null) {\n            if (pNode == root) {\n                root = pNode.rchild;\n            } else if (pNode == pNode.parent.lchild) {\n                pNode.parent.lchild = pNode.rchild;\n                pNode.rchild.parent = pNode.parent;\n            } else if (pNode == pNode.parent.rchild) {\n                pNode.parent.rchild = pNode.rchild;\n                pNode.rchild.parent = pNode.parent;\n            }\n        }\n        // 如果要删除的节点只有左节点\n        if (pNode.lchild != null && pNode.rchild == null) {\n            if (pNode == root) {\n                root = pNode.lchild;\n            } else if (pNode == pNode.parent.lchild) {\n                pNode.parent.lchild = pNode.lchild;\n                pNode.lchild.parent = pNode.parent;\n            } else if (pNode == pNode.parent.rchild) {\n                pNode.parent.rchild = pNode.lchild;\n                pNode.lchild.parent = pNode.parent;\n            }\n        }\n\n        // 第三种情况： （删除有两个子节点的节点，即左右子节点都非空）\n\n        // 方法是用要删除的节点的后续节点代替要删除的节点，并且删除后续节点（删除后续节点的时候需要递归操作）\n        // 解析：这里要用到的最多也就会发生两次，即后续节点不会再继续递归的删除下一个后续节点了，\n        // 因为，要删除的节点的后续节点肯定是:要删除的那个节点的右子树的最小关键字，而这个最小关键字肯定不会有左节点;\n        // 所以，在删除后续节点的时候肯定不会用到（两个节点都非空的判断 ），如有有子节点，肯定就是有一个右节点。\n        if (pNode.lchild != null && pNode.rchild != null) {\n            // 先找出后续节点\n            TreeNode successorNode = successor(pNode);\n            if (pNode == root) {\n                root.key = successorNode.key;\n            } else {\n                pNode.key = successorNode.key;// 赋值，将后续节点的值赋给要删除的那个节点\n            }\n            delete(successorNode);// 递归的删除后续节点\n        }\n    }\n```\n\n\n### 5. 二叉搜索树的遍历\n\n前序遍历：[leetcode第144题](https://github.com/apachecn/LeetCode/blob/master/docs/Leetcode_Solutions/144._binary_tree_preorder_traversal.md)\n\n中序遍历：[leetcode第94题](https://github.com/apachecn/LeetCode/blob/master/docs/Leetcode_Solutions/094._binary_tree_inorder_traversal.md)\n\n后序遍历：[leetcode第145题](https://github.com/apachecn/LeetCode/blob/master/docs/Leetcode_Solutions/145._binary_tree_postorder_traversal.md)\n\n\n层次遍历：[leetcode第102题](https://github.com/apachecn/LeetCode/blob/master/docs/Leetcode_Solutions/102._binary_tree_level_order_traversal.md)\n\n## References\n\n[数据结构与算法之二叉搜索树插入、查询与删除](https://blog.csdn.net/chenliguan/article/details/52956546)\n\n[二叉搜索树的插入与删除图解](http://www.cnblogs.com/MrListening/p/5782752.html)\n\n[二叉树算法删除代码实现](https://blog.csdn.net/tayanxunhua/article/details/11100113)\n\n[二叉树的Java实现及特点总结](http://www.cnblogs.com/lzq198754/p/5857597.html)\n\n\n\n\n\n\n\n\n\n\n\n\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/位运算.md",
    "content": "### 位运算\n\n位运算包括: 加 减 乘 取反 and 异或 \n\n- 0110 + 0110 = 0110 * 2 ，也就是0110左移1位\n\n- 0011 * 0100  0100 = 4， 一个数乘以 2^n 即是将这个数左移n\n\n- a ^(~a) = 0\n\n- x & (~0 << n ) 这样来看，0取反全部为1，然后将其右移n位，后面的全是0，x & (~0 <<n)， 后边的全为0， clear the rightmost n bits of x\n\n  ​\n\nPython的符号\n\n- &：按位与\n\n-  |：按位或\n\n-  ^：按位异或\n\n- ~：取反\n\n-  <<：左移\n\n- \\>>：右移\n\n  ​\n\nBit Facts and Tricks\n\n```\nx ^ 0s = x\t\tx & 0s = 0\t\tx | 0s = x\nx ^ 1s = ~x \tx & 1s = x\t\tx | 1s = 1s\nx ^ x = 0\t\tx & x = x \t\tx | x = x\n```\n\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/全排列算法.md",
    "content": "### 全排列算法\n\n\n#### 46. Permutations\n\n\nGiven a collection of distinct numbers, return all possible permutations.\n\nFor example,\n[1,2,3] have the following permutations:\n\n\t[\n\t  [1,2,3],\n\t  [1,3,2],\n\t  [2,1,3],\n\t  [2,3,1],\n\t  [3,1,2],\n\t  [3,2,1]\n\t]\n\t\n\t\n#####从空开始加\n\n先跳离开这道题，来看类似的'ABC',我们要求它的全排列\n\n\n```\ndef recPermute(sofar, rest):\n\tif rest == '':\n\t\tprint sofar\n\telse:\n\t\tfor i in range(len(rest)):\n\t\t\tnxt = sofar + rest[i]\n\t\t\tremaining = rest[:i] + rest[i+1:]\n\t\t\trecPermute(nxt, remaining)\n\n// \"wrapper\" function\ndef listPermute(s):\n\trecPermute('',s)\n```\n\n会正确输出`ABC ACB BAC BCA CAB CBA`,题目依靠的是每次我们从余下的字母中选一个，如果画图则会是这样:\n\n\n```\n\t\tA\t\t\t\tB\t\t\t\tC\n\tB\t\tC\t\tA\t\tC\t\tA\t\tB\n\tC\t\tB\t\tC\t\tA\t\tB\t\tA\n```\n\n时间复杂度应该是O(n!)\n\n- n choose 1\n- n-1 choose 1\n- ...\n\n\n\n#####另一种市面上常见思路是交换：\n\n思路是这样的,同样看上面的图:\n\n- n个元素的全排列 = （n-1）个元素的全排列 + 另一个元素作为前缀\n- 如果只有一个元素，那么这个元素本身就是它的全排列\n- 不断将每个元素放作第一个元素，然后将这个元素作为前缀，并将其余元素继续全排列，等到出口，出口出去后还需要还原数组\n\n\n这个用数组来测试更容易写代码和直观：\n\n\n```\ndef recPermute(nums,begin):\n    n = len(nums)\n    if begin == n:\n    \tprint nums,\n    \n    for i in range(begin,n):\n        nums[begin], nums[i] = nums[i],nums[begin]\n        recPermute(nums,begin+1)\n        nums[begin],nums[i] = nums[i],nums[begin]\n\nrecPermute(['A','B','C'],0)\n\n```\n\n这样的写法更容易理解：\n\n\n```python\nclass Solution:\n    # @param num, a list of integer\n    # @return a list of lists of integers\n    def permute(self, num):\n        if len(num) == 0: return []\n        if len(num) == 1: return [num]\n        res = []\n        for i in range(len(num)):\n            x  = num[i]\n            xs = num[:i] + num[i+1:]             \n            for j in self.permute(xs):\n                res.append([x] + j)\n        return res\n```\n\n每次用一个没有用过的头元素，然后加上全排列产生的结果.\n\n如果分析复杂度，应该也是O(n!)\n\n\n#### 47. Permutations II\n\n\n最简单的想法：\n\n- 排序\n- 如果碰到重复的就继续处理下一个\n\n```\nclass Solution(object):\n    def permuteUnique(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: List[List[int]]\n        \"\"\"\n        if len(nums) == 0: return []\n        if len(nums) == 1: return [nums]\n        res = []\n        nums.sort()\n        for i in range(len(nums)):\n            if i > 0 and nums[i] == nums[i-1]: continue\n            for j in self.permuteUnique(nums[:i] + nums[i+1:]):\n                res.append([nums[i]] + j)\n        return res\n\n```\n\n\n\n\n#### 31. Next Permutation\n\n实际上这个题目也就是Generation in lexicographic order,\n\nwikipedia 和 [这里](https://www.nayuki.io/page/next-lexicographical-permutation-algorithm) 有很好，很精妙的算法，也有点two pointer的意思\n\n\n```\n1. Find the highest index i such that s[i] < s[i+1]. If no such index exists, the permutation is the last permutation.\n2. Find the highest index j > i such that s[j] > s[i]. Such a j must exist, since i+1 is such an index.\n3. Swap s[i] with s[j].\n4. Reverse the order of all of the elements after index i till the last element.\n```\n\n\n看例子：\n\n125430\n\n\n- 从末尾开始，找到decreasing subsequence，5430，因为来调5330无论怎么调，都不可能有比它更小的，数也被自然的分成两部分(1,2) 和 （5，4，3，0)\n- 下一步是找这个sequence里面第一个比前面部分，比2大的，3，也很容易理解，因为下一个必定是(1,3)打头\n- 交换 3和2 ，变成 (1,3,5,4,2,0),再把后面的部分reverse，得到后面部分可得到的最小的\n\n这个时候，得到下一个sequence 130245\n\n\n\n```\nclass Solution(object):\n    def nextPermutation(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: void Do not return anything, modify nums in-place instead.\n        \"\"\"\n        m, n = 0, 0\n        for i in range(len(nums) - 2, 0 , -1):\n        \tif nums[i] < nums[i+1]:\n        \t\tm = i \n        \t\tbreak\n\n        for i in range(len(nums) - 1, 0 , -1):\n        \tif nums[i] > nums[m]:\n        \t\tn = i\n        \t\tbreak\n\n        if m < n :\n        \tnums[m], nums[n] = nums[n], nums[m]\n        \tnums[m+1:] = nums[len(nums):m:-1]\n        else:\n        \tnums = nums.reverse()\n```\n\n\n所以可以用这个next permutation来解46/47也可以，然后我兴奋了一下，这个算法很快的！然后我又冷静了，因为permutation的个数是O(n!)个啊|||,所以也不可能有啥大的提升吧\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/八排序.md",
    "content": "## 前言\n八大排序，三大查找是《数据结构》当中非常基础的知识点，在这里为了复习顺带总结了一下常见的八种排序算法。\n常见的八大排序算法，他们之间关系如下：\n\n![](/images/SortingAlgorithm/八大排序算法总结.png)\n\n他们的性能比较：\n\n![](/images/SortingAlgorithm/八大排序算法性能.png)\n\n### 直接插入排序 (Insertion sort)\n\n![](/images/SortingAlgorithm/直接插入排序.gif)\n\n直接插入排序的核心思想就是：将数组中的所有元素依次跟前面已经排好的元素相比较，如果选择的元素比已排序的元素小，则交换，直到全部元素都比较过。\n因此，从上面的描述中我们可以发现，直接插入排序可以用两个循环完成：\n\n1. 第一层循环：遍历待比较的所有数组元素\n2. 第二层循环：将本轮选择的元素(selected)与已经排好序的元素(ordered)相比较。\n    - 如果```selected > ordered```，那么将二者交换\n\n```python\n#直接插入排序\ndef insert_sort(L):\n    #遍历数组中的所有元素，其中0号索引元素默认已排序，因此从1开始\n    for x in range(1,len(L)):\n    #将该元素与已排序好的前序数组依次比较，如果该元素小，则交换\n    #range(x-1,-1,-1):从x-1倒序循环到0\n        for i in range(x-1,-1,-1):\n    #判断：如果符合条件则交换\n            if L[i] > L[i+1]:\n                L[i], L[i+1] = L[i+1], L[i]\n```\n### 希尔排序 (Shell sort)\n\n![](/images/SortingAlgorithm/希尔排序.png)\n\n希尔排序的算法思想：将待排序数组按照步长gap进行分组，然后将每组的元素利用直接插入排序的方法进行排序；每次将gap折半减小，循环上述操作；当gap=1时，利用直接插入，完成排序。\n同样的：从上面的描述中我们可以发现：希尔排序的总体实现应该由三个循环完成：\n\n1. 第一层循环：将gap依次折半，对序列进行分组，直到gap=1\n2. 第二、三层循环：也即直接插入排序所需要的两次循环。具体描述见上。\n\n```python\n#希尔排序\ndef insert_shell(L):\n    #初始化gap值，此处利用序列长度的一半为其赋值\n    gap = int(len(L)/2)\n    #第一层循环：依次改变gap值对列表进行分组\n    while (gap >= 1):\n    #下面：利用直接插入排序的思想对分组数据进行排序\n    #range(gap,len(L)):从gap开始\n        for x in range(gap,len(L)):\n    #range(x-gap,-1,-gap):从x-gap开始与选定元素开始倒序比较，每个比较元素之间间隔gap\n            for i in range(x-gap,-1,-gap):\n    #如果该组当中两个元素满足交换条件，则进行交换\n                if L[i] > L[i+gap]:\n                    L[i], L[i+gap] = L[i+gap], L[i]\n    #while循环条件折半\n        gap = int((gap/2))\n```\n\n### 简单选择排序 (Selection sort)\n\n![](/images/SortingAlgorithm/简单选择排序.gif)\n\n简单选择排序的基本思想：比较+交换。\n\n1. 从待排序序列中，找到关键字最小的元素；\n2. 如果最小元素不是待排序序列的第一个元素，将其和第一个元素互换；\n3. 从余下的 N - 1 个元素中，找出关键字最小的元素，重复(1)、(2)步，直到排序结束。\n因此我们可以发现，简单选择排序也是通过两层循环实现。\n    - 第一层循环：依次遍历序列当中的每一个元素\n    - 第二层循环：将遍历得到的当前元素依次与余下的元素进行比较，符合最小元素的条件，则交换。\n\n```python\n# 简单选择排序\ndef select_sort(L):\n#依次遍历序列中的每一个元素\n    for x in range(0,len(L)):\n#将当前位置的元素定义此轮循环当中的最小值\n        minimum = L[x]\n#将该元素与剩下的元素依次比较寻找最小元素\n        for i in range(x+1,len(L)):\n            if L[i] < minimum:\n                L[i], minimum = minimum, L[i]\n#将比较后得到的真正的最小值赋值给当前位置\n        L[x] = minimum\n```\n\n### 堆排序 (Heap sort)\n\n#### 堆的概念\n\n堆：本质是一种数组对象。特别重要的一点性质：<b>任意的叶子节点小于（或大于）它所有的父节点</b>。对此，又分为大顶堆和小顶堆，大顶堆要求节点的元素都要大于其孩子，小顶堆要求节点元素都小于其左右孩子，两者对左右孩子的大小关系不做任何要求。\n利用堆排序，就是基于大顶堆或者小顶堆的一种排序方法。下面，我们通过大顶堆来实现。\n\n基本思想：\n堆排序可以按照以下步骤来完成：\n\n1. 首先将序列构建称为大顶堆；\n\n（这样满足了大顶堆那条性质：位于根节点的元素一定是当前序列的最大值）\n\n![](/images/SortingAlgorithm/构建大顶堆.png)\n\n2. 取出当前大顶堆的根节点，将其与序列末尾元素进行交换；\n\n（此时：序列末尾的元素为已排序的最大值；由于交换了元素，当前位于根节点的堆并不一定满足大顶堆的性质）\n\n3. 对交换后的n-1个序列元素进行调整，使其满足大顶堆的性质；\n\n![](/images/SortingAlgorithm/调整大顶堆.png)\n\n4. 重复2.3步骤，直至堆中只有1个元素为止\n\n```python\n#-------------------------堆排序--------------------------------\n#**********获取左右叶子节点**********\ndef LEFT(i):\n    return 2*i + 1\ndef RIGHT(i):\n    return 2*i + 2\n#********** 调整大顶堆 **********\n#L:待调整序列 length: 序列长度 i:需要调整的结点\ndef adjust_max_heap(L, length, i):\n#定义一个int值保存当前序列最大值的下标\n    largest = i\n#获得序列左右叶子节点的下标\n    left, right = LEFT(i), RIGHT(i)\n#当左叶子节点的下标小于序列长度 并且 左叶子节点的值大于父节点时，将左叶子节点的下标赋值给largest\n    if (left < length) and (L[left] > L[i]):\n        largest = left\n#当右叶子节点的下标小于序列长度 并且 右叶子节点的值大于父节点时，将右叶子节点的下标值赋值给largest\n    if (right < length) and (L[right] > L[largest]):\n        largest = right\n#如果largest不等于i 说明当前的父节点不是最大值，需要交换值\n    if (largest != i):\n        L[i], L[largest] = L[largest], L[i]\n        # 执行递归操作：两个任务：1 寻找最大值的下标；2.最大值与父节点交换\n        adjust_max_heap(L, length, largest)\n#********** 建立大顶堆 **********\ndef build_max_heap(L):\n    length = len(L)\n    for x in range(int((length-1)/2), -1, -1):\n        adjust_max_heap(L, length, x)\n#********** 堆排序 **********\ndef heap_sort(L):\n#先建立大顶堆，保证最大值位于根节点；并且父节点的值大于叶子结点\n    build_max_heap(L)\n#i：当前堆中序列的长度.初始化为序列的长度\n    i = len(L)\n#执行循环：1. 每次取出堆顶元素置于序列的最后(len-1,len-2,len-3...)\n#         2. 调整堆，使其继续满足大顶堆的性质，注意实时修改堆中序列的长度\n    while (i > 0):\n        L[i-1], L[0] = L[0], L[i-1]\n#堆中序列长度减1\n        i -= 1\n#调整大顶堆\n        adjust_max_heap(L, i, 0)\n```\n### 冒泡排序 (Bubble sort)\n\n![](/images/SortingAlgorithm/冒泡排序.gif)\n\n冒泡排序思路比较简单：\n\n1. 将序列当中的左右元素，依次比较，保证右边的元素始终大于左边的元素；\n（ 第一轮结束后，序列最后一个元素一定是当前序列的最大值；）\n2. 对序列当中剩下的n-1个元素再次执行步骤1。\n3. 对于长度为n的序列，一共需要执行n-1轮比较\n（利用while循环可以减少执行次数）\n\n```python\n#冒泡排序\ndef bubble_sort(L):\n    length = len(L)\n#序列长度为length，需要执行length-1轮交换\n    for x in range(1, length):\n#对于每一轮交换，都将序列当中的左右元素进行比较\n#每轮交换当中，由于序列最后的元素一定是最大的，因此每轮循环到序列未排序的位置即可\n        for i in range(0, length-x):\n            if L[i] > L[i+1]:\n                L[i], L[i+1] = L[i+1], L[i]\n```\n\n### 快速排序 (Quick sort)\n\n![](/images/SortingAlgorithm/快速排序.gif)\n\n快速排序的基本思想：挖坑填数+分治法\n\n1. 从序列当中选择一个基准数(pivot)\n在这里我们选择序列当中第一个数作为基准数\n2. 将序列当中的所有数依次遍历，比基准数大的位于其右侧，比基准数小的位于其左侧\n3. 重复步骤1.2，直到所有子集当中只有一个元素为止。\n\n用伪代码描述如下：\n- i =L; j = R; 将基准数挖出形成第一个坑a[i]。\n- j--由后向前找比它小的数，找到后挖出此数填前一个坑a[i]中。\n- i++由前向后找比它大的数，找到后也挖出此数填到前一个坑a[j]中。\n- 再重复执行2，3二步，直到i==j，将基准数填入a[i]中\n\n```python\n#快速排序\n#L：待排序的序列；start排序的开始index,end序列末尾的index\n#对于长度为length的序列：start = 0;end = length-1\ndef quick_sort(L, start, end):\n    if start < end:\n        i, j, pivot = start, end, L[start]\n        while i < j:\n#从右开始向左寻找第一个小于pivot的值\n            while (i < j) and (L[j] >= pivot):\n                j -= 1\n#将小于pivot的值移到左边\n            if (i < j):\n                L[i] = L[j]\n                i += 1\n#从左开始向右寻找第一个大于pivot的值\n            while (i < j) and (L[i] <= pivot):\n                i += 1\n#将大于pivot的值移到右边\n            if (i < j):\n                L[j] = L[i]\n                j -= 1\n#循环结束后，说明 i=j，此时左边的值全都小于pivot,右边的值全都大于pivot\n#pivot的位置移动正确，那么此时只需对左右两侧的序列调用此函数进一步排序即可\n#递归调用函数：依次对左侧序列：从0 ~ i-1//右侧序列：从i+1 ~ end\n        L[i] = pivot\n#左侧序列继续排序\n        quick_sort(L, start, i-1)\n#右侧序列继续排序\n        quick_sort(L, i+1, end)\n```\n\n### 归并排序 (Merge sort)\n\n![](/images/SortingAlgorithm/归并排序.gif)\n\n1. 归并排序是建立在归并操作上的一种有效的排序算法，该算法是采用分治法的一个典型的应用。它的基本操作是：将已有的子序列合并，达到完全有序的序列；即先使每个子序列有序，再使子序列段间有序。\n2. 归并排序其实要做两件事：\n    - 分解----将序列每次折半拆分\n    - 合并----将划分后的序列段两两排序合并\n因此，归并排序实际上就是两个操作，拆分+合并\n3. 如何合并？\n    - L[first...mid]为第一段，L[mid+1...last]为第二段，并且两端已经有序，现在我们要将两端合成达到L[first...last]并且也有序。\n    - 首先依次从第一段与第二段中取出元素比较，将较小的元素赋值给temp[]\n    - 重复执行上一步，当某一段赋值结束，则将另一段剩下的元素赋值给temp[]\n    - 此时将temp[]中的元素复制给L[]，则得到的L[first...last]有序\n4. 如何分解？\n    - 在这里，我们采用递归的方法，首先将待排序列分成A,B两组；\n    - 然后重复对A、B序列分组；\n    - 直到分组后组内只有一个元素，此时我们认为组内所有元素有序，则分组结束。\n\n```python\n# 归并排序\n#这是合并的函数\n# 将序列L[first...mid]与序列L[mid+1...last]进行合并\ndef mergearray(L, first, mid, last, temp):\n#对i,j,k分别进行赋值\n    i, j, k = first, mid+1, 0\n#当左右两边都有数时进行比较，取较小的数\n    while (i <= mid) and (j <= last):\n        if L[i] <= L[j]:\n            temp[k] = L[i]\n            i += 1\n            k += 1\n        else:\n            temp[k] = L[j]\n            j += 1\n            k += 1\n#如果左边序列还有数\n    while (i <= mid):\n        temp[k] = L[i]\n        i += 1\n        k += 1\n#如果右边序列还有数\n    while (j <= last):\n        temp[k] = L[j]\n        j += 1\n        k += 1\n#将temp当中该段有序元素赋值给L待排序列使之部分有序\n    for x in range(0, k):\n        L[first+x] = temp[x]\n# 这是分组的函数\ndef merge_sort(L, first, last, temp):\n    if first < last:\n        mid = int(((first + last) / 2))\n#使左边序列有序\n        merge_sort(L, first, mid, temp)\n#使右边序列有序\n        merge_sort(L, mid+1, last, temp)\n#将两个有序序列合并\n        mergearray(L, first, mid, last, temp)\n# 归并排序的函数\ndef merge_sort_array(L):\n#声明一个长度为len(L)的空列表\n    temp = len(L)*[None]\n#调用归并排序\n    merge_sort(L, 0, len(L)-1, temp)\n```\n\n### 基数排序 (Radix sort)\n\n![](/images/SortingAlgorithm/基数排序.gif)\n\n1. 基数排序：通过序列中各个元素的值，对排序的N个元素进行若干趟的“分配”与“收集”来实现排序。\n    - 分配：我们将L[i]中的元素取出，首先确定其个位上的数字，根据该数字分配到与之序号相同的桶中\n    - 收集：当序列中所有的元素都分配到对应的桶中，再按照顺序依次将桶中的元素收集形成新的一个待排序列L[ ]\n    - 对新形成的序列L[]重复执行分配和收集元素中的十位、百位...直到分配完该序列中的最高位，则排序结束\n2. 根据上述“基数排序”的展示，我们可以清楚的看到整个实现的过程\n\n```python\n#************************基数排序****************************\n#确定排序的次数\n#排序的顺序跟序列中最大数的位数相关\ndef radix_sort_nums(L):\n    maxNum = L[0]\n#寻找序列中的最大数\n    for x in L:\n        if maxNum < x:\n            maxNum = x\n#确定序列中的最大元素的位数\n    times = 0\n    while (maxNum > 0):\n        maxNum = int((maxNum/10))\n        times += 1\n    return times\n#找到num从低到高第pos位的数据\ndef get_num_pos(num, pos):\n    return (int((num/(10**(pos-1))))) % 10\n#基数排序\ndef radix_sort(L):\n    count = 10 * [None]       #存放各个桶的数据统计个数\n    bucket = len(L) * [None]  #暂时存放排序结果\n#从低位到高位依次执行循环\n    for pos in range(1, radix_sort_nums(L)+1):\n        #置空各个桶的数据统计\n        for x in range(0, 10):\n            count[x] = 0\n        #统计当前该位(个位，十位，百位....)的元素数目\n        for x in range(0, len(L)):\n            #统计各个桶将要装进去的元素个数\n            j = get_num_pos(int(L[x]), pos)\n            count[j] += 1\n        #count[i]表示第i个桶的右边界索引\n        for x in range(1,10):\n            count[x] += count[x-1]\n        #将数据依次装入桶中\n        for x in range(len(L)-1, -1, -1):\n            #求出元素第K位的数字\n            j = get_num_pos(L[x], pos)\n            #放入对应的桶中，count[j]-1是第j个桶的右边界索引\n            bucket[count[j]-1] = L[x]\n            #对应桶的装入数据索引-1\n            count[j] -= 1\n        # 将已分配好的桶中数据再倒出来，此时已是对应当前位数有序的表\n        for x in range(0, len(L)):\n            L[x] = bucket[x]\n```\n\n## 运行时间实测\n\n10w数据\n```\n直接插入排序:1233.581131\n希尔排序:1409.8012320000003\n简单选择排序:466.66974500000015\n堆排序:1.2036720000000969\n冒泡排序:751.274449\n#****************************************************\n快速排序:1.0000003385357559e-06\n#快速排序有误：实际上并未执行\n#RecursionError: maximum recursion depth exceeded in comparison\n#****************************************************\n归并排序:0.8262230000000272\n基数排序:1.1162899999999354\n```\n从运行结果上来看，堆排序、归并排序、基数排序真的快。\n对于快速排序迭代深度超过的问题，可以将考虑将快排通过非递归的方式进行实现。\n\n## Resources\n\n1. [算法导论》笔记汇总](http://mindlee.com/2011/08/21/study-notes-directory/)\n2. [八大排序算法的 Python 实现](http://python.jobbole.com/82270/)\n3. [数据结构常见的八大排序算法（详细整理）](https://www.jianshu.com/p/7d037c332a9d)\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/子集合问题.md",
    "content": "###子集合问题\n\n####78. Subsets\n\n子集合是全排列的好朋友,也是combination组合的好朋友，排列·组合·子集，他们三个都是好朋友.\n\n\n#####从空开始加\n\n同样先来看'ABC'\n\n```\ndef recsubsets(sofar, rest):\n\tif rest == '':\n\t\tprint sofar, \n\telse:\n\t\trecsubsets(sofar, rest[1:])\n\t\trecsubsets(sofar + rest[0], rest[1:])\n\ndef listsubsets(s):\n\trecsubsets('',s)\n\n\nlistsubsets('ABC')\n```\n\n##### 市面流行思路\n\n市面上流行的思路：\n\n- [[],[1]] 是 [1] 的子集合\n- [[],[1],[2],[1,2]] 是 [1,2] 的子集合，实际上就是1的子集合们加了一个2\n\n\n所以用python写起来也很简单/精美\n\n```\ndef subsets(nums):\n    \"\"\"\n    :type nums: List[int]\n    :rtype: List[List[int]]\n    \"\"\"\n    results = [[]]\n    for num in nums:\n        results.extend([result + [num] for result in results])\n    return results\n```\n我在这里犯过错，所以这一句\n\n`results.extend([result + [num] for result in results])` 实际上等于：\n\n\n```\ntmp = []\nfor result in results:\n    tmp.append(result + [num])\nresults.extend(tmp)\n```\n\n<http://stackoverflow.com/questions/38600315/python-power-set-cant-figure-out-my-error>\n\n\n\n#### 90. Subsets II\n\n\n要去重了,比如如果有 [1,2,2]，那么解答为：\n\n\n\t[\n\t  [2],\n\t  [1],\n\t  [1,2,2],\n\t  [2,2],\n\t  [1,2],\n\t  []\n\t]\n\n\n现在来观察规律，与之前有不同之处是我们需要一个位置来mark，因为不再需要往之前出现过的地方再加了，看这个:\n\n\n```\n[[],[1]] 是 [1] 的子集合\n[[],[1],[2],[1,2]] 是 [1,2] 的子集合，实际上就是1的子集合们加了一个2\n新来的2不能再从头开始加了，它需要从[ .., [2],[1,2] ]加 才是合理的\n```\n\n所以看到非常精妙的代码\n\n\n```\ndef subsets(nums):\n    \"\"\"\n    :type nums: List[int]\n    :rtype: List[List[int]]\n    \"\"\"\n    nums.sort()\n    result = [[]]\n    temp_size = 0\n\n    for i in range(len(nums)):\n    \tstart = temp_size if i >= 1 and nums[i] == nums[i-1] else 0\n    \ttemp_size = len(result)\n    \t#print start,temp_size,result\n    \tfor j in range(start, temp_size):\n    \t\tresult.append(result[j] + [nums[i]])\n    print result\n\nsubsets([1,2,2])\n```\n\n这里这个start是来记录了之前一次数组的长度，temp_size记住目前数组的长度，然后用这个来达到去重的目的，非常聪明\n\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/总结.md",
    "content": "# 1\n```solution```下自定义函数```func(self, fargs, *args, **kwargs)```, 调用时使用```self.func()```的格式\n\n# 2\n```not fargs``` 和 ```fargs == None```不一样，前者```fargs```可能为[], '', 0, etc\n\n# 3\n递归问题\nAny problem can be solved using dp. Solving using a greedy strategy is harder though, since you need to prove that greedy will work for that problem. There are some tell-tale signs of a problem where greedy may be applicable, but isn’t immediately apparent. Example:\n\n- Choice of an element depends only on its immediate neighbours (wiggle sort).\n- Answer is monotonically non-decreasing or non-increasing (sorting). This is also applicable for LIS for example.\n- Anything that requires lexicographically largest or smallest of something.\n- Anything where processing the input in sorted order will help.\n- Anything where processing the input in forward or reverse (as given) will help.\n- Anything which requires you to track the minimum or maximum of something (think of sliding window problems).\n\nThere’s matroid theory which deal with greedy algorithms, but I don’t really understand it. If someone does, I’ll be super grateful to them to explain it to me in simple language!\n\nIn general, try to see if for a problem, the solution doesn’t depend on a lot of history about the solution itself, but the next part of the solution is somewhat independent from the rest of the solution. These are all indicative of the fact that a greedy strategy could be applicable.\n\n# 4\n[Counter.elements()](https://docs.python.org/2/library/collections.html)\n\n# 5\n测试案例写法\n\n```python\nimport unittest\nclass Solution(object):\n    def isMatch(self, s, p):\n        \"\"\"\n        :type s: str\n        :type p: str\n        :rtype: bool\n        \"\"\"\n        m, n = len(s), len(p)\n        dp = [ [0 for i in range(n+1)] for j in range(m+1)]\n\n        dp[0][0] = 1\n\n        # init the first line\n        for i in range(2,n+1):\n            if p[i-1] == '*':\n                dp[0][i] = dp[0][i-2]\n\n        for i in range(1,m+1):\n            for j in range(1,n+1):\n                if p[j-1] == '*':\n                    if p[j-2] != s[i-1] and p[j-2] != '.':\n                        dp[i][j] = dp[i][j-2]\n                    elif p[j-2] == s[i-1] or p[j-2] == '.':\n                        dp[i][j] = dp[i-1][j] or dp[i][j-2]\n\n                elif s[i-1] == p[j-1] or p[j-1] == '.':\n                    dp[i][j] = dp[i-1][j-1]\n\n        return dp[m][n] == 1\n\n\nclass TestSolution(unittest.TestCase):\n    def test_none_0(self):\n        s = \"\"\n        p = \"\"\n        self.assertTrue(Solution().isMatch(s, p))\n\n    def test_none_1(self):\n        s = \"\"\n        p = \"a\"\n        self.assertFalse(Solution().isMatch(s, p))\n\n    def test_no_symbol_equal(self):\n        s = \"abcd\"\n        p = \"abcd\"\n        self.assertTrue(Solution().isMatch(s, p))\n\n    def test_no_symbol_not_equal_0(self):\n        s = \"abcd\"\n        p = \"efgh\"\n        self.assertFalse(Solution().isMatch(s, p))\n\n    def test_no_symbol_not_equal_1(self):\n        s = \"ab\"\n        p = \"abb\"\n        self.assertFalse(Solution().isMatch(s, p))\n\n    def test_symbol_0(self):\n        s = \"\"\n        p = \"a*\"\n        self.assertTrue(Solution().isMatch(s, p))\n\n    def test_symbol_1(self):\n        s = \"a\"\n        p = \"ab*\"\n        self.assertTrue(Solution().isMatch(s, p))\n\n    def test_symbol_2(self):\n        # E.g.\n        #   s a b b\n        # p 1 0 0 0\n        # a 0 1 0 0\n        # b 0 0 1 0\n        # * 0 1 1 1\n        s = \"abb\"\n        p = \"ab*\"\n        self.assertTrue(Solution().isMatch(s, p))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n    \n    \n\n输出：\n........\n\nRan 8 tests in 0.001s\n\nOK\n```\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/组合问题.md",
    "content": "### 组合问题\n\n\n#### 77.Combinations\n\n\n##### 会超时的recursion\n\n\n\n```\nclass Solution(object):\n    def combine(self, n, k):\n        \"\"\"\n        :type n: int\n        :type k: int\n        :rtype: List[List[int]]\n        \"\"\"\n        ans = []\n        self.dfs(n, k, 1, [], ans)\n        return ans\n\n    def dfs(self, n, k ,start, lst, ans):\n    \tif k == 0 :\n    \t\tans.append(lst)\n    \t\treturn\n    \tfor i in range(start, n+1):\n    \t\tself.dfs(n, k - 1, i + 1,lst +[i], ans)\n```\n\n理解方式\n\n```\n\n\t\t\t\t\t1          2     3\n\t\t\t    12  13 14    23 24   34\t\t\t\n```\n\n可以参照这里\n\n\n<http://www.geeksforgeeks.org/print-all-possible-combinations-of-r-elements-in-a-given-array-of-size-n/>\n\n\n##### 市面上流行解法\n\n递归的思想： n选k\n\n- 如果 k==n ，则全选。\n- n > k 又可以分成两类：\n\t- 选了n， 则在余下的n-1中选k-1\n\t- 没有选n， 则在余下的n-1中选k \n\n注意一下会有两个base case，因为k在不断减小和n在不断减小，所以写起来可以这样：\n\n\n```\ndef combine(n,k):\n    if k == 1:\n        return [[i+1] for i in range(n)]\n    if n == k:\n        return [range(1, k+1)]\n    # choose n , not choose n \n    return [r + [n] for r in combine(n-1,k-1)] + combine(n-1,k)\n\n\nprint combine(20,16)\n```\n\n\n#### 39. Combination Sum\n\n\n使用正常递归思路\n\n\n#### 40. Combination Sum II \n\n重复做跳过处理\n\n#### 216. Combination Sum III\n\n\n#### 377. Combination Sum IV\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/递归_recursion.md",
    "content": "#递归 Recursion\n\n### 递归\n\n递归绝对是一个非常重要的概念。比如安利？ 不断的delegate，本来想要完成1000个人的销售，找10个人，每人完成100人的，这10个人每人再去找10个人，每人完成10人的销售，这样就完成了1000人的销售（不懂安利是否这样，拿来举例）。\n\n\n递归之所以重要，这里面存在的概念太多了，首先上面这个例子里面就有divide and conquer的意思，把task divide小，然后来解决它。\n\n\n同样有趣的例子 → 吃完一个bowl of chips：\n\n- for loop，知道多少薯片，然后从0开始吃到最后\n- while， while 碗里还有薯片，就吃\n- 递归，吃一片，然后继续吃剩下的 N - 1 片，直到碗里的薯片数量只剩下0片了\n\n\n典型的例子：\n\n- pow(x,n)\n- isPalindrome\n- TowerofHanoi\n- binarySearch\n\n\n\n### 链表， 树， 图\n\n链表（linked list） 是数据结构的基础，而链表本身就是具有递归特性的,看C++中对于linked list node的定义, next指向本身这样的结构，就是再这个node定义还未完成之时，我们已经指向自己。\n\n\n```\nstruct node{\n    int data;\n    node* next;\n};\n```\n\nbinary tree定义就是靠递归来实现的。\n"
  },
  {
    "path": "docs/Algorithm/DataStructure/Summarization/面试确认题目细节问题.md",
    "content": "1. The length of the given array is positive and will not exceed 20?\n2. The sum of elements in the given array will not exceed 1000?\n3. The output answer is guaranteed to be fitted in a 32-bit integer？\n"
  },
  {
    "path": "docs/Algorithm/ProjectCornerstone/ApproveLetter.md",
    "content": "\n![](https://github.com/apachecn/Interview/tree/master/docs/Algorithm/ProjectCornerstone/3221532952133_.pic_hd.jpg)\n![](https://github.com/apachecn/Interview/tree/master/docs/Algorithm/ProjectCornerstone/3231532952152_.pic_hd.jpg)\n"
  },
  {
    "path": "docs/Algorithm/README.md",
    "content": "# LeetCode 面试必备\n\n> **欢迎任何人参与和完善：一个人可以走的很快，但是一群人却可以走的更远**\n\n* 英文官网: https://leetcode.com\n* 中文官网: https://leetcode-cn.com\n* [ApacheCN 组织资源](https://docs.apachecn.org/): <https://docs.apachecn.org/>\n* **ApacheCN - 面试求职群【724187166】<a target=\"_blank\" href=\"//shang.qq.com/wpa/qunwpa?idkey=9bcf2fb3985835c9c2f15783ec9c85822e23be1191a6581eaf22f574b5192b19\"><img border=\"0\" src=\"http://data.apachecn.org/img/logo/ApacheCN-group.png\" alt=\"ApacheCN - 面试求职群[724187166]\" title=\"ApacheCN - 面试求职群[724187166]\"></a>**\n\n## 关于刷题\n\n```\n刷题可能是目前来说：最有用，也是最没用的东西\n\n有用只是指：面试最快捷的一种方式\n最没用是指：基本上在工作中用不上\n\n简单来说；形式主义为主，技术提升为辅，目的就是为了驯服和奴役思维\n\n会刷题和当年会考试没有本质区别\n我并没有觉得这个是一件值得骄傲的事情\n相反，这恰恰是普通人没用选择的事情\n\n我吐槽一下：\n工作5年，面试还要刷题，写排序，聊一些优化细节和技巧，感觉比较惭愧\n你能写出一个高性能的代码吗？（例如：处理10G，做排序）\n你反问：为什么不用GPU和Spark.\n他说：。。（他无语）\n我觉得大家可能都很无语吧，最无语的应该是前沿的技术吧\n\n\n很多时候，基本上你遇到问题百度一下答案就出来了\n工作中你可能没遇到，但是面试你必须背下来，不管会不会\n\n一个人的好坏，我觉得是在人的性格和搜索能力。\n但是在各大公司的HR和不入流的面试官面前：高学历和强刷题\n技术高低和花的时间有关系，而工作是否录取和高学历和强刷题有关\n\n```\n\n---\n\n当然我们得先战胜市场，才能改变市场！\n下面正式开始我们刷题教程入门 -- 你准备好了吗？\n\n## 数据结构 - 排序\n\n* 二分查找\n* 冒泡排序\n* 插入排序\n* 选择排序\n* 快速排序\n* 希尔排序\n* 归并排序\n* 基数排序\n\n实战入口: <https://interview.apachecn.org/docs/Algorithm/DataStructure>\n\n## [算法刷题](https://github.com/apachecn/Interview/tree/master/docs/Algorithm/README.md)\n\n1. [Leetcode](/docs/Algorithm/Leetcode)\n    - [Python](/docs/Algorithm/Leetcode/Python)\n    - [Java](/docs/Algorithm/Leetcode/Java)\n    - [JavaScript](/docs/Algorithm/Leetcode/JavaScript)\n    - [C++](/docs/Algorithm/Leetcode/C++)\n    - [ipynb](/docs/Algorithm/Leetcode/ipynb)\n    - [GO](https://github.com/aQuaYi/LeetCode-in-Go)\n    - [Golang](https://github.com/kylesliu/awesome-golang-leetcode)\n2. [剑指 Offer](/docs/Algorithm/剑指offer)\n    - [Python](/docs/Algorithm/剑指offer/Python)\n    - [Scala](/docs/Algorithm/剑指offer/Scala)\n    - [Java](/docs/Algorithm/剑指offer/Java)\n    - [JavaScript](/docs/Algorithm/剑指offer/JavaScript)\n    - [C++](/docs/Algorithm/剑指offer/C++)\n3. [数据结构](/docs/Algorithm/DataStructure)\n    - [Wikipedia: List of Algorithms](https://en.wikipedia.org/wiki/List_of_algorithms)\n\n## 参与方式\n\n> 提交PR基本要求（满足任意一种即可）\n\n* 不一样的思路\n* 优化时间复杂度和空间复杂度，或者解决题目的Follow up\n* 有意义的简化代码\n* 未提交过的题目\n\n> **案例模版**\n\n[模版 md: 001. Two Sum 两数之和](docs/Algorithm/Leetcode/Python/001._two_sum.md)\n[模版页面效果: 001. Two Sum 两数之和](https://interview.apachecn.org/docs/Algorithm/Leetcode/Python/001._two_sum.html)\n\n## 推荐 LeetCode 网站\n\n- [KrisYu的Github](https://github.com/KrisYu/LeetCode-CLRS-Python)\n- [kamyu104的Github](https://github.com/kamyu104/LeetCode)\n- [数据结构与算法/leetcode/lintcode题解](https://algorithm.yuanbin.me/zh-hans/)\n- [Leetcode 讨论区](https://discuss.leetcode.com/)\n- [visualgo算法可视化网站](https://visualgo.net/en)\n- [Data Structure Visualization](https://www.cs.usfca.edu/~galles/visualization/Algorithms.html)\n- [我的算法学习之路 - Lucida](http://zh.lucida.me/blog/on-learning-algorithms/)\n- [HiredInTech](https://www.hiredintech.com/) System Design 的总结特别适合入门\n- [mitcc的Github](https://github.com/mitcc/AlgoSolutions)\n- [小土刀的面试刷题笔记](http://wdxtub.com/interview/14520594642530.html)\n- [nonstriater/Learn-Algorithms](https://github.com/nonstriater/Learn-Algorithms)\n- [剑指 Offer 题解](https://github.com/gatieme/CodingInterviews)\n- https://github.com/liuchuo/LeetCode\n- https://github.com/anxiangSir/SwordforOffer\n- https://www.nowcoder.com/ta/coding-interviews?page=1\n- [【小姐姐】刷题博客](https://www.liuchuo.net/about)\n- [公瑾的Github](https://github.com/yuzhoujr/leetcode)\n- [shejie1993](https://shenjie1993.gitbooks.io/leetcode-python/content/096%20Unique%20Binary%20Search%20Trees.html)\n- [编程之法：面试和算法心得](https://legacy.gitbook.com/book/wizardforcel/the-art-of-programming-by-july/details)\n- [算法/NLP/深度学习/机器学习面试笔记](https://github.com/imhuay/Interview_Notes-Chinese)\n"
  },
  {
    "path": "docs/GitHub/README.md",
    "content": "# GitHub 入门须知\n\n## 基本介绍\n\n* git 是一个版本控制工具.\n* Github 是一个基于 git 的社会化代码分享社区, 所谓 social coding.\n*开源项目是可以免费托管到GitHub上面，在GitHub上你基本上可以找到任何你想要的代码。\n\n## 基本用途\n\n| 名称 | 描述 |\n| - | - |\n| 个人写作 | 个人笔记（支持 .md 格式）<br/>Note: 写书，可以去 GitBook |\n| 多人写作 | 1.适合内容协同和迭代<br/> 2.适合即将步入职场的人熟悉职场操作 |\n| 搭建网站 | GitHub Pages 用于免费搭建个人网站，支持绑定个人域名 | \n| 个人简历 | 你会发现，GitHub 是一个不错的求职敲门砖，可以理解为程序员的个人简历 |\n| 开源项目 | 开源社区流行着一句话叫「不要重复发明轮子」，也就是站在巨人的肩膀上看得更远 | \n\n## 下载地址\n\n### 1.客户端安装(例如：Mac 三种方式都可行)\n\n> (一 客户端操作: https://desktop.github.com/  【不推荐，程序员建议命令行】\n\n> (二 通过 homebrew 安装 git\n\n* 1.安装homebrew\n\n```\n/usr/bin/ruby -e \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)\"\n```\n\n* 2.安装 git\n\n```\nbrew install git\n```\n\n> (三 通过 Xcode 安装\n\n* 直接从 AppStore 安装 Xcode, Xcode 集成了 Git.\n* 不过默认没有安装, 你需要运行 Xcode, 选择菜单 Xcode -> Preferences.\n* 在弹出窗口中找到 Downloads, 选择 Command Line Tools, 点 Install 就可以完成安装了.\n\n### 2.创建 ssh key、 配置 git\n\n> 1、设置 username 和 email（github 每次 commit 都会记录他们）\n\n```\ngit config --global user.name \"jiangzhonglian\"\ngit config --global user.email \"jiang-s@163.com\"\n```\n\n> 2、通过终端命令创建 ssh key\n\n```\nssh-keygen -t rsa -C \"jiang-s@163.com\"\n```\n\n`jiang-s@163.com` 是我的邮件名，回车会有以下输出\n\n```\nLast login: Sat Jan  6 14:12:16 on ttys000\njiangzl@jiangzhongliandeMacBook-Pro:~$ ssh-keygen -t rsa -C \"jiang-s@163.com\"\nGenerating public/private rsa key pair.\nEnter file in which to save the key (/Users/jiangzl/.ssh/id_rsa): \n/Users/jiangzl/.ssh/id_rsa already exists.\nOverwrite (y/n)? n\njiangzl@jiangzhongliandeMacBook-Pro:~$\n```\n\n* 这里我原来已经创建过，这里我选 n.\n* 没有创建过的就选择 y, 我们默认的一路回车就行（有特殊癖好: 可以确认路径和输入密码） .\n* 成功的话会在 ~/ 下生成 .ssh 文件夹, 进去打开 id_rsa.pub, 复制里面的key.\n* 终端查看 `.ssh/id_rsa.pub` 文件, 然后复制里面的key.\n\n### 3.上传 ssh key 到 GitHub 服务器上面\n\n> 1.注册 GitHub 帐号， 登录GitHub\n\n> 2.点击 Settings, 添加ssh key\n\n![](/img/docs/GitHub/GitHub-Setting.png) \n\n> 3.点击 New SSH key\n\n![](/img/docs/GitHub/SSH-Key.jpg) \n\n> 4.链接验证\n\n```\nssh -T git@github.com \n```\n\n终端输出结果\n\n```\nLast login: Sat Jan  6 14:42:55 on ttys000\njiangzl@jiangzhongliandeMacBook-Pro:~$ ssh -T git@github.com \nHi jiangzhonglian! You've successfully authenticated, but GitHub does not provide shell access.\njiangzl@jiangzhongliandeMacBook-Pro:~$\n```\n\n说明已经链接成功。\n\n## 基本命令\n\n<a href=\"https://www.bilibili.com/video/av15705305/\" target=\"_blank\">此图片链接为 bilibili 视频地址: (视频图片下面为文本操作指南)\n<img src=\"/img/docs/GitHub/ApacheCN-GitHub入门操作-Fork到PullRequests.png\">\n</a>\n\n全局概况图(看不懂就多看几遍)\n\n![](/img/docs/GitHub/github_origin_online_remote.jpg)\n\n> 一) fork apachecn/kaggle 项目\n\n![](/img/docs/GitHub/github-step-1-fork.jpg)\n\n![](/img/docs/GitHub/github-step-2-clone.jpg)\n\n\n> 二) jiangzhonglian/kaggle 第一次初始化\n\n可以使用 vscode 进行可视化操作\n\n```\nclone 自己的 repo 仓库  （这个是自己的地址， jiangzhonglian是我的，别弄错了）\n$ git clone https://github.com/jiangzhonglian/kaggle.git\n\n## 进入该仓库的文件夹\n$ cd kaggle\n\n## 查看该仓库远程 repo\n$ git remote\n\n## 添加 apachecn 的远程 repo 仓库（添加一次以后就不用使用了）\n$ git remote add origin_online https://github.com/apachecn/kaggle.git\n```\n\n> 三) jiangzhonglian/kaggle 文件更新（修改文件后，第二次要进行提交）\n\n```\n# 用于 pull 保持和 apachecn 同步\n$ git pull origin_online master\n\n#上传到 自己的 repo 仓库\n$ git push origin master\n```\n\n> 四) pull requests 到 apachecn/kaggle\n\n![](/img/docs/GitHub/github-step-3-PullRequests.jpg)\n\n![](/img/docs/GitHub/github-step-4-PullRequests.jpg)\n\n![](/img/docs/GitHub/github-step-5-PullRequests.jpg)\n\n"
  },
  {
    "path": "docs/Kaggle/README.md",
    "content": "# Kaggle\n\n![](img/logos/kaggle-logo-gray-bigger.jpeg)\n\n> **你已经抓住了石头，现在是挥舞它的时候了！**\n\n* [ApacheCN 组织资源](https://docs.apachecn.org/): <https://docs.apachecn.org/>\n* **ApacheCN - 比赛学习群【724187166】<a target=\"_blank\" href=\"//shang.qq.com/wpa/qunwpa?idkey=9bcf2fb3985835c9c2f15783ec9c85822e23be1191a6581eaf22f574b5192b19\"><img border=\"0\" src=\"http://data.apachecn.org/img/logo/ApacheCN-group.png\" alt=\"ApacheCN - 比赛学习群[724187166]\" title=\"ApacheCN - 比赛学习群[724187166]\"></a>**\n* [Kaggle](https://www.kaggle.com) 是一个流行的数据科学竞赛平台。\n* [GitHub 入门操作指南](/docs/GitHub/README.md) 和 [Kaggle 入门操作指南](/docs/kaggle-quickstart.md)，适合于学习过 [MachineLearning(机器学习实战)](https://github.com/apachecn/MachineLearning) 的小盆友\n* Kaggle 已被 Google 收购，请参阅[《谷歌收购 Kaggle 为什么会震动三界（AI、机器学习、数据科学界）》](https://www.leiphone.com/news/201703/ZjpnddCoUDr3Eh8c.html)\n\n> Note:\n\n* 号外号外 [**kaggle组队开始啦**](/docs/kaggle-start.md)\n* 比赛收集平台: <https://github.com/iphysresearch/DataSciComp>\n* [关于 ApacheCN](https://home.apachecn.org/about/): 一边学习和整理，一边录制项目视频，希望能组建一个开源的公益团队对国内机器学习社区做一些贡献，同时也为装逼做准备!!\n\n## 直播系列\n\n* https://space.bilibili.com/97678687/channel/detail?cid=76173\n\n> kaggle入门系列\n\n* [Kaggle系列-数字识别](https://www.bilibili.com/video/av53119200)\n* [Kaggle系列-泰坦尼克号](https://www.bilibili.com/video/av65679428)\n\n> 比赛直播系列\n\n* [视频: 2019ICME 抖音视频理解 top2 solution 分享及 数据比赛入门讲解](https://www.bilibili.com/video/av57385532)\n* [文档: icme2019-top2.pptx](/docs/简历指南/icme2019-top2.pptx)\n* [昊神GitHub地址: https://github.com/Smilexuhc](https://github.com/Smilexuhc)\n* [昊神整理比赛系列: https://github.com/Smilexuhc/Data-Competition-TopSolution](https://github.com/Smilexuhc/Data-Competition-TopSolution)\n\n## Kaggle 官方教程\n\n> 机器学习入门\n\n* [**1. 模型是怎样工作的**](learn/intro-to-machine-learning/1.md)\n* [**2. 数据探索**](learn/intro-to-machine-learning/2.md)\n* [**3. 你的第一个机器学习模型**](learn/intro-to-machine-learning/3.md)\n* [**4. 模型验证**](learn/intro-to-machine-learning/4.md)\n* [**5. 欠拟合与过拟合**](learn/intro-to-machine-learning/5.md)\n* [**6. 随机森林**](learn/intro-to-machine-learning/6.md)\n* [**7. 继续你的征程**](learn/intro-to-machine-learning/7.md)\n\n> 补充\n\n* [**Embedding**](learn/embeddings)\n\n\n## [竞赛](https://www.kaggle.com/competitions)\n\n* 【推荐】特征工程全过程: https://www.cnblogs.com/jasonfreak/p/5448385.html\n\n> train loss 与 test loss 结果分析\n\n* train loss 不断下降，test loss不断下降，说明网络仍在学习;\n* train loss 不断下降，test loss趋于不变，说明网络过拟合;\n* train loss 趋于不变，test loss不断下降，说明数据集100%有问题;\n* train loss 趋于不变，test loss趋于不变，说明学习遇到瓶颈，需要减小学习率或批量数目;\n* train loss 不断上升，test loss不断上升，说明网络结构设计不当，训练超参数设置不当，数据集经过清洗等问题。\n\n```python\n机器学习比赛，奖金很高，业界承认分数。\n现在我们已经准备好尝试 Kaggle 竞赛了，这些竞赛分成以下几个类别。\n```\n\n### [第1部分：课业比赛 InClass](https://www.kaggle.com/competitions?sortBy=deadne&group=all&page=1&pageSize=20&segment=inClass)\n\n`课业比赛 InClass` 是学校教授机器学习的老师留作业的地方，这里的竞赛有些会向public开放参赛，也有些仅仅是学校内部教学使用。\n\n### [第2部分：入门比赛 Getting Started](https://www.kaggle.com/competitions?sortBy=deadline&group=all&page=1&pageSize=20&segment=gettingStarted)\n\n`入门比赛 Getting Started` 给萌新们一个试水的机会，没有奖金，但有非常多的前辈经验可供学习。很久以前Kaggle这个栏目名称是101的时候，比赛题目还很多，但是现在只保留了9个最经典的入门竞赛：手写数字识别、沉船事故幸存估计、脸部识别、Julia语言入门。\n\n* [**数字识别**](competitions/getting-started/digit-recognizer)\n* [**泰坦尼克**](competitions/getting-started/titanic)\n* [**房价预测**](competitions/getting-started/house-price)\n* [**nlp-情感分析**](competitions/getting-started/word2vec-nlp-tutorial)\n\n### [第3部分：训练场 Playground](https://www.kaggle.com/competitions?sortBy=deadline&group=all&page=1&pageSize=20&segment=playground)\n\n`训练场 Playground`里的题目以有趣为主，比如猫狗照片分类的问题。现在这个分类下的题目不算多，但是热度很高。\n\n* [**猫狗识别**](competitions/playground/dogs-vs-cats)\n\n### [第4部分： 研究项目(少奖金) Research](https://www.kaggle.com/competitions?sortBy=prize&group=active&page=1&pageSize=20&segment=research)\n\n`研究型 Research` 竞赛通常是机器学习前沿技术或者公益性质的题目。竞赛奖励可能是现金，也有一部分以会议邀请、发表论文的形式奖励。\n\n### [第5部分：人才征募 Recruitment](https://www.kaggle.com/competitions?sortBy=prize&group=active&page=1&pageSize=20&segment=recruitment)\n\n`人才征募 Recruitment` 竞赛是赞助企业寻求数据科学家、算法设计人才的渠道。只允许个人参赛，不接受团队报名。\n\n### [第6部分： 大型组织比赛(大奖金) Featured](https://www.kaggle.com/competitions?sortBy=prize&group=active&page=1&pageSize=20&segment=featured)\n\n`推荐比赛 Featured` 是瞄准商业问题带有奖金的公开竞赛。如果有幸赢得比赛，不但可以获得奖金，模型也可能会被竞赛赞助商应用到商业实践中呢。\n\n* [**Mercari 价格推荐挑战**](competitions/featured/mercari-price-suggestion-challenge)\n* [**Home Credit Default Risk**](competitions/featured/home-credit-default-risk)\n\n### [第7部分： 限量邀请赛 Masters（新）](https://www.kaggle.com/competitions?sortBy=grouped&group=general&page=1&pageSize=20&category=masters)\n\n`Masters（新）`  限量参与比赛（受邀）\n\n### [第8部分： 多评估标准赛 Analytics（新）](https://www.kaggle.com/competitions?sortBy=grouped&group=general&page=1&pageSize=20&category=analytics)\n\n`Analytics（新）` 选择最优评估标准来排名的比赛\n\n### 天池\n\n* [**天池入门教程: O2O优惠券-使用新人赛**](https://tianchi.aliyun.com/notebook/detail.html?spm=5176.11409386.4851167.7.65c91d07FiVHVN&id=4796)\n* [**天池第一名: O2O优惠券-预测用户领取优惠劵后是否核销**](https://github.com/wepe/O2O-Coupon-Usage-Forecast)\n\n## 其他部分\n\n* [数据集](https://www.kaggle.com/datasets): 数据集，可直接用于机器学习。\n* [核心思想](https://www.kaggle.com/kernels): 在线编程。（猜测，基于 jupyter 实现）\n* [论坛](https://www.kaggle.com/discussion): 发帖回帖讨论的平台\n* [**学习 - 新**](https://www.kaggle.com/learn/overview): 最新发布的学习教程\n* [招聘](https://www.kaggle.com/jobs): 企业招聘数据科学家的位置\n\n## 解决方案列表\n\n* [解决方案列表](/docs/writeup-list.md)\n\n如果解决方案太大，可以先放在这个列表中。以后再逐步整合到这个仓库。\n\n## 机器学习算法\n\n> 常用算法选择\n\n![](img/docs/kaggle-常用算法选择.png)\n\n> 常用工具选择\n\n![](img/docs/kaggle-常用工具选择.png)\n\n> 解决问题的流程\n\n1. 链接场景和目标\n2. 链接评估准则\n3. 认识数据\n4. 数据预处理（清洗、调权）\n5. 特征工程\n6. 模型调参\n7. 模型状态分析\n8. 模型融合\n\n> 数据预处理\n\n* 数据清洗\n    * 去掉样本数据的异常数据。（比如连续型数据中的离群点）\n    * 去除缺失大量特征的数据\n* 数据采样\n    * 下/上采样（假设正负样本比例1:100，把正样本的数量重复100次，这就叫上采样，也就是把比例小的样本放大。下采样同理，把比例大的数据抽取一部分，从而使比例变得接近于1；1）\n    * 保证样本均衡\n* 工具 sql、pandas等\n\n> 特征工程\n\n![](img/docs/kaggle-特征工程.png)\n\n> 特征处理\n\n- 数值型：连续型数据离散化或者归一化、数据变化（log、指数、box-cox）\n- 类别型：做编码，eg：one-hot编码，如果类别数据有缺失，把缺失也作为一个类别即可。\n- 时间类：间隔化（距离某个节日多少天）、与其他特征（eg：次数）融合，变成一周登陆几次、离散化（eg：外卖，把时间分为【饭店、非饭店】）\n- 文本类：N-gram、Bag-of-words、TF-IDF\n- 统计型：与业务强关联\n- 组合特征\n\n## 贡献指南\n\n> **欢迎任何人参与和完善：一个人可以走的很快，但是一群人却可以走的更远**\n\n本项目接受大家提交 WriteUp（题解）。\n\nWriteUp 需要带有预处理过程，从你能下载到的原始数据开始，并且带有验证过程和评价指标。\n\n请放在`/competitions/{分类}/{名称}`目录下。\n\n其中分类一共有六个，请见上面，名称是 URL 中`/c/`后面的部分。\n"
  },
  {
    "path": "docs/Kaggle/competitions/featured/home-credit-default-risk/ManualFeatureEngineering_P1.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 简介：手动特征工程 part 1\\n\",\n    \"\\n\",\n    \"如果你是初次参与这个竞赛，我强烈建议你先阅读这个文档https://www.kaggle.com/willkoehrsen/start-here-a-gentle-introduction/\\n\",\n    \"\\n\",\n    \"在这份文档中，我们首先会研究如何通过手工的方式，处理捷信公司贷款违约风险问题的相关特征。在更早之前的一篇文章中，我们仅仅使用了贷款申请数据来建模。我们利用该数据所建立的最佳模型在排行榜上的分数大约是0.74。为了提高这个分数，我们需要从其他数据中引入更多的信息，包括bureau和bureau_balance数据。数据的定义如下：\\n\",\n    \"bureau:捷信公司(Home Credit，下同)所掌握的客户从其他金融机构中贷款的历史信息。其中每一行代表一条历史贷款信息\\n\",\n    \"bureau_balance:历史贷款的月度信息。其中每一行代表一条月度信息\\n\",\n    \"\\n\",\n    \"手动特征工程是一个很无聊的过程（这就是我们为什么需要使用自动化的特征处理工具来处理我们的数据），而且该过程经常需要依靠该领域的专家知识来辅助处理。由于我对贷款行业和客户违约原因知之甚少，所以我的关注点集中在如何收集尽量多的数据来训练模型，原因在于模型会代替我们来评估哪些数据比较重要。简而言之，我们的方法就是获得尽量多的数据提供给模型使用！接着我们可以利用特征的重要性或是PCA降维等方法来进行特征约减\\n\",\n    \"\\n\",\n    \"进行手动特征工程的过程需要大量Pandas代码，一点点耐心和许多操作数据的实践。尽管自动化的特性工程工具已经十分遍历，但是特性工程仍然需要使用大量数据清洗重组(data wrangle)一段时间才能完成\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#使用 pandas 和 numpy 进行数据处理\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"#使用 matplotlib 和 seaborn 进行绘制\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"\\n\",\n    \"#禁止 pandas 的编译警告\\n\",\n    \"import warnings\\n\",\n    \"warnings.filterwarnings('ignore')\\n\",\n    \"\\n\",\n    \"plt.style.use('fivethirtyeight')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 案例：计算客户历史贷款总数\\n\",\n    \"\\n\",\n    \"为了解释常见的手工特征工程流程，我们首先计算一个客户在其他金融机构的历史贷款信息总和，这一过程会用到许多我们接下去会频繁使用到的Pandas操作：\\n\",\n    \"\\n\",\n    \"* groupby: 按列对数据进行分组。在这个案例中我们根据具有唯一性的SK_ID_CURR列进行分组【按列进行分组？不是按行吗？】\\n\",\n    \"* agg: 对分组后的数据进行计算，例如列均值。我们可以直接使用方程(grouped_df.mean())或者将agg方程与转换后的列表一起使用(grouped_df.agg([mean, max, min, sum]))\\n\",\n    \"* merge: 将聚合后的数据与对应的客户匹配。我们需要将原始训练数据与计算后的SK_ID_CURR列数据结合在一起，如果SK_ID_CURR列没有对应的用户数据，则置为NaN。\\n\",\n    \"\\n\",\n    \"我们还经常使用（重命名）函数来将指定的列(columns)重命名为字典(dictionary)，这有利于对新创建的特征保持观察\\n\",\n    \"\\n\",\n    \"这看起来有点复杂，所以最终需要构造一个函数来代替我们执行这个过程。但首先，我们需要了解如何手工实现这个过程\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th>SK_ID_BUREAU</th>\\n\",\n       \"      <th>CREDIT_ACTIVE</th>\\n\",\n       \"      <th>CREDIT_CURRENCY</th>\\n\",\n       \"      <th>DAYS_CREDIT</th>\\n\",\n       \"      <th>CREDIT_DAY_OVERDUE</th>\\n\",\n       \"      <th>DAYS_CREDIT_ENDDATE</th>\\n\",\n       \"      <th>DAYS_ENDDATE_FACT</th>\\n\",\n       \"      <th>AMT_CREDIT_MAX_OVERDUE</th>\\n\",\n       \"      <th>CNT_CREDIT_PROLONG</th>\\n\",\n       \"      <th>AMT_CREDIT_SUM</th>\\n\",\n       \"      <th>AMT_CREDIT_SUM_DEBT</th>\\n\",\n       \"      <th>AMT_CREDIT_SUM_LIMIT</th>\\n\",\n       \"      <th>AMT_CREDIT_SUM_OVERDUE</th>\\n\",\n       \"      <th>CREDIT_TYPE</th>\\n\",\n       \"      <th>DAYS_CREDIT_UPDATE</th>\\n\",\n       \"      <th>AMT_ANNUITY</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>215354</td>\\n\",\n       \"      <td>5714462</td>\\n\",\n       \"      <td>Closed</td>\\n\",\n       \"      <td>currency 1</td>\\n\",\n       \"      <td>-497</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-153.0</td>\\n\",\n       \"      <td>-153.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>91323.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>Consumer credit</td>\\n\",\n       \"      <td>-131</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>215354</td>\\n\",\n       \"      <td>5714463</td>\\n\",\n       \"      <td>Active</td>\\n\",\n       \"      <td>currency 1</td>\\n\",\n       \"      <td>-208</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1075.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>225000.0</td>\\n\",\n       \"      <td>171342.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>Credit card</td>\\n\",\n       \"      <td>-20</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>215354</td>\\n\",\n       \"      <td>5714464</td>\\n\",\n       \"      <td>Active</td>\\n\",\n       \"      <td>currency 1</td>\\n\",\n       \"      <td>-203</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>528.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>464323.5</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>Consumer credit</td>\\n\",\n       \"      <td>-16</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>215354</td>\\n\",\n       \"      <td>5714465</td>\\n\",\n       \"      <td>Active</td>\\n\",\n       \"      <td>currency 1</td>\\n\",\n       \"      <td>-203</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>90000.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>Credit card</td>\\n\",\n       \"      <td>-16</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>215354</td>\\n\",\n       \"      <td>5714466</td>\\n\",\n       \"      <td>Active</td>\\n\",\n       \"      <td>currency 1</td>\\n\",\n       \"      <td>-629</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1197.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>77674.5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2700000.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>Consumer credit</td>\\n\",\n       \"      <td>-21</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_CURR  SK_ID_BUREAU CREDIT_ACTIVE CREDIT_CURRENCY  DAYS_CREDIT  \\\\\\n\",\n       \"0      215354       5714462        Closed      currency 1         -497   \\n\",\n       \"1      215354       5714463        Active      currency 1         -208   \\n\",\n       \"2      215354       5714464        Active      currency 1         -203   \\n\",\n       \"3      215354       5714465        Active      currency 1         -203   \\n\",\n       \"4      215354       5714466        Active      currency 1         -629   \\n\",\n       \"\\n\",\n       \"   CREDIT_DAY_OVERDUE  DAYS_CREDIT_ENDDATE  DAYS_ENDDATE_FACT  \\\\\\n\",\n       \"0                   0               -153.0             -153.0   \\n\",\n       \"1                   0               1075.0                NaN   \\n\",\n       \"2                   0                528.0                NaN   \\n\",\n       \"3                   0                  NaN                NaN   \\n\",\n       \"4                   0               1197.0                NaN   \\n\",\n       \"\\n\",\n       \"   AMT_CREDIT_MAX_OVERDUE  CNT_CREDIT_PROLONG  AMT_CREDIT_SUM  \\\\\\n\",\n       \"0                     NaN                   0         91323.0   \\n\",\n       \"1                     NaN                   0        225000.0   \\n\",\n       \"2                     NaN                   0        464323.5   \\n\",\n       \"3                     NaN                   0         90000.0   \\n\",\n       \"4                 77674.5                   0       2700000.0   \\n\",\n       \"\\n\",\n       \"   AMT_CREDIT_SUM_DEBT  AMT_CREDIT_SUM_LIMIT  AMT_CREDIT_SUM_OVERDUE  \\\\\\n\",\n       \"0                  0.0                   NaN                     0.0   \\n\",\n       \"1             171342.0                   NaN                     0.0   \\n\",\n       \"2                  NaN                   NaN                     0.0   \\n\",\n       \"3                  NaN                   NaN                     0.0   \\n\",\n       \"4                  NaN                   NaN                     0.0   \\n\",\n       \"\\n\",\n       \"       CREDIT_TYPE  DAYS_CREDIT_UPDATE  AMT_ANNUITY  \\n\",\n       \"0  Consumer credit                -131          NaN  \\n\",\n       \"1      Credit card                 -20          NaN  \\n\",\n       \"2  Consumer credit                 -16          NaN  \\n\",\n       \"3      Credit card                 -16          NaN  \\n\",\n       \"4  Consumer credit                 -21          NaN  \"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 读入bureau属性\\n\",\n    \"bureau = pd.read_csv('bureau.csv')\\n\",\n    \"bureau.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th>previous_loan_counts</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>100001</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>100002</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>100003</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>100004</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>100005</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_CURR  previous_loan_counts\\n\",\n       \"0      100001                     7\\n\",\n       \"1      100002                     8\\n\",\n       \"2      100003                     4\\n\",\n       \"3      100004                     2\\n\",\n       \"4      100005                     3\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 按照客户id (SK_ID_CURR) 进行Groupeby操作，计算历史贷款总数，并将SK_ID_BUREAU列重命名为previous_loan_counts\\n\",\n    \"previous_loan_counts = bureau.groupby('SK_ID_CURR', as_index = False)['SK_ID_BUREAU'].count().rename(columns = {'SK_ID_BUREAU': 'previous_loan_counts'})\\n\",\n    \"previous_loan_counts.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th>TARGET</th>\\n\",\n       \"      <th>NAME_CONTRACT_TYPE</th>\\n\",\n       \"      <th>CODE_GENDER</th>\\n\",\n       \"      <th>FLAG_OWN_CAR</th>\\n\",\n       \"      <th>FLAG_OWN_REALTY</th>\\n\",\n       \"      <th>CNT_CHILDREN</th>\\n\",\n       \"      <th>AMT_INCOME_TOTAL</th>\\n\",\n       \"      <th>AMT_CREDIT</th>\\n\",\n       \"      <th>AMT_ANNUITY</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>FLAG_DOCUMENT_19</th>\\n\",\n       \"      <th>FLAG_DOCUMENT_20</th>\\n\",\n       \"      <th>FLAG_DOCUMENT_21</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\\n\",\n       \"      <th>previous_loan_counts</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>100002</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>202500.0</td>\\n\",\n       \"      <td>406597.5</td>\\n\",\n       \"      <td>24700.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>8.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>100003</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>270000.0</td>\\n\",\n       \"      <td>1293502.5</td>\\n\",\n       \"      <td>35698.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>100004</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Revolving loans</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>67500.0</td>\\n\",\n       \"      <td>135000.0</td>\\n\",\n       \"      <td>6750.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>100006</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>135000.0</td>\\n\",\n       \"      <td>312682.5</td>\\n\",\n       \"      <td>29686.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>100007</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>121500.0</td>\\n\",\n       \"      <td>513000.0</td>\\n\",\n       \"      <td>21865.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 123 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\\\\n\",\n       \"0      100002       1         Cash loans           M            N   \\n\",\n       \"1      100003       0         Cash loans           F            N   \\n\",\n       \"2      100004       0    Revolving loans           M            Y   \\n\",\n       \"3      100006       0         Cash loans           F            N   \\n\",\n       \"4      100007       0         Cash loans           M            N   \\n\",\n       \"\\n\",\n       \"  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\\\\n\",\n       \"0               Y             0          202500.0    406597.5      24700.5   \\n\",\n       \"1               N             0          270000.0   1293502.5      35698.5   \\n\",\n       \"2               Y             0           67500.0    135000.0       6750.0   \\n\",\n       \"3               Y             0          135000.0    312682.5      29686.5   \\n\",\n       \"4               Y             0          121500.0    513000.0      21865.5   \\n\",\n       \"\\n\",\n       \"           ...           FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\\\\n\",\n       \"0          ...                          0                0                0   \\n\",\n       \"1          ...                          0                0                0   \\n\",\n       \"2          ...                          0                0                0   \\n\",\n       \"3          ...                          0                0                0   \\n\",\n       \"4          ...                          0                0                0   \\n\",\n       \"\\n\",\n       \"  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\\\\n\",\n       \"0                        0.0                       0.0   \\n\",\n       \"1                        0.0                       0.0   \\n\",\n       \"2                        0.0                       0.0   \\n\",\n       \"3                        NaN                       NaN   \\n\",\n       \"4                        0.0                       0.0   \\n\",\n       \"\\n\",\n       \"  AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\\\\n\",\n       \"0                        0.0                        0.0   \\n\",\n       \"1                        0.0                        0.0   \\n\",\n       \"2                        0.0                        0.0   \\n\",\n       \"3                        NaN                        NaN   \\n\",\n       \"4                        0.0                        0.0   \\n\",\n       \"\\n\",\n       \"   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  previous_loan_counts  \\n\",\n       \"0                        0.0                         1.0                   8.0  \\n\",\n       \"1                        0.0                         0.0                   4.0  \\n\",\n       \"2                        0.0                         0.0                   2.0  \\n\",\n       \"3                        NaN                         NaN                   0.0  \\n\",\n       \"4                        0.0                         0.0                   1.0  \\n\",\n       \"\\n\",\n       \"[5 rows x 123 columns]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 将计算得到的previous_loan_counts列加入原先的训练数据application_train.csv\\n\",\n    \"train = pd.read_csv('application_train.csv')\\n\",\n    \"train = train.merge(previous_loan_counts, on = 'SK_ID_CURR', how = 'left')# 左连接，左侧DataFrame取全部，右侧DataFrame取部分\\n\",\n    \"\\n\",\n    \"# 填补缺失值\\n\",\n    \"train['previous_loan_counts'] = train['previous_loan_counts'].fillna(0)\\n\",\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"向左滑动查看新插入的列\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 使用r值检验方法评估新属性（变量）\\n\",\n    \"为了确定新加入的变量是否有用，我们计算了该变量与目标值之间的皮尔森相关系数(Pearson Correlation Coeffcient, r-value)。该系数可以评估两个变量之间的线性关系强弱，范围在[-1, 1]之间。r值检验方法并不是检验新变量“有效性”最好的方法，但是它可以给出一个新变量对于机器学习模型是否有帮助的初步估计。新变量r值检验的值越大，那么该变量的改变对于目标值的影响就越大。因此,我们希望寻找到与目标值的之间，皮尔森相关系数绝对值最大的新变量\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 核密度估计图\\n\",\n    \"核密度估计图展示的是一个单独变量的分布（可以将其看做一个平滑的直方图），为了看到某个分类中变量的值对分布函数的影响，我们根据类别对不同的分布使用不同的颜色标注。例如，我们可以通过目标值为1或0来判断previous_loan_count变量的核密度估计，所得到的KDE可以显示出未偿还贷款的人(TARGET==1)和偿还贷款的人(TARGET==0)之间变量分布的所有显著性差异，这将作为变量是否与机器学习模型相关的指标\\n\",\n    \"\\n\",\n    \"我们将把这个绘图功能放在一个函数中，以便对任何变量重复使用\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 绘制根据不同目标值着色的变量分布\\n\",\n    \"def kde_target(var_name, df):\\n\",\n    \"    \\n\",\n    \"    # 计算变量与目标值之间的皮尔森相关系数\\n\",\n    \"    corr = df['TARGET'].corr(df[var_name])\\n\",\n    \"    \\n\",\n    \"    # 计算偿还或未偿还贷款数据的中位数\\n\",\n    \"    avg_repaid = df.ix[df['TARGET'] == 0, var_name].median()# df.ix：索引函数，既可以通过行号索引，也可以通过行标签索引\\n\",\n    \"    avg_not_repaid = df.ix[df['TARGET'] == 1, var_name].median()\\n\",\n    \"    \\n\",\n    \"    plt.figure(figsize = (12, 6))\\n\",\n    \"    \\n\",\n    \"    # 绘制偿还或未偿还贷款数据的分布\\n\",\n    \"    sns.kdeplot(df.ix[df['TARGET'] == 0, var_name], label = 'TARGET == 0')\\n\",\n    \"    sns.kdeplot(df.ix[df['TARGET'] == 1, var_name], label = 'TARGET == 1')\\n\",\n    \"    \\n\",\n    \"    # 标签绘制\\n\",\n    \"    plt.xlabel(var_name); plt.ylabel('Density'); plt.title('%s Distribution' % var_name)\\n\",\n    \"    plt.legend();# 显示图例\\n\",\n    \"    \\n\",\n    \"    # 输出皮尔森相关系数\\n\",\n    \"    print('The correlation between %s and the TARGET is %0.4f' % (var_name, corr))\\n\",\n    \"    # 输出均值\\n\",\n    \"    print('Median value for loan that was not repaid = %0.4f' % avg_not_repaid)\\n\",\n    \"    print('Median value for loan that was repaid =     %0.4f' % avg_repaid)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们可以利用EXT_SOURCE_3变量来测试该方程，这是根据随机森林和梯度提升机得出的最佳变量https://www.kaggle.com/willkoehrsen/start-here-a-gentle-introduction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The correlation between EXT_SOURCE_3 and the TARGET is -0.1789\\n\",\n      \"Median value for loan that was not repaid = 0.3791\\n\",\n      \"Median value for loan that was repaid =     0.5460\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1774f3236d8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"kde_target('EXT_SOURCE_3', train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"接下来对新变量previous_loan_counts进行同样的操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The correlation between previous_loan_counts and the TARGET is -0.0100\\n\",\n      \"Median value for loan that was not repaid = 3.0000\\n\",\n      \"Median value for loan that was repaid =     4.0000\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1774f323240>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"kde_target('previous_loan_counts', train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"从图上看，很难说这个变量是否重要。相关系数很弱并且分布几乎没有什么显著差异\\n\",\n    \"\\n\",\n    \"让我们继续从bureau数据中尝试生成更多变量，我们考虑每个数值列的均值，最小值和最大值 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 聚合数值列\\n\",\n    \"为了考虑bureau数据集中的数值信息，我们对所有数值列做一些计算处理。\\n\",\n    \"为此，我们将数据按照用户id分组(groupby)，分组后的数据再进行聚合(agg)操作，最后将结果合并(merge)到训练数据集中。由于聚合(agg)操作只能在数值型数据中使用，所有我们只计算数值列的值。在这里我们依然使用均值(mean)、最大值(max)、最小值(min)和总和(sum)函数，但事实上任何函数都可以在agg函数中被调用，甚至包括自己编写的函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead tr th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th colspan=\\\"5\\\" halign=\\\"left\\\">DAYS_CREDIT</th>\\n\",\n       \"      <th colspan=\\\"4\\\" halign=\\\"left\\\">CREDIT_DAY_OVERDUE</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th colspan=\\\"5\\\" halign=\\\"left\\\">DAYS_CREDIT_UPDATE</th>\\n\",\n       \"      <th colspan=\\\"5\\\" halign=\\\"left\\\">AMT_ANNUITY</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>100001</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-735.000000</td>\\n\",\n       \"      <td>-49</td>\\n\",\n       \"      <td>-1572</td>\\n\",\n       \"      <td>-5145</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-93.142857</td>\\n\",\n       \"      <td>-6</td>\\n\",\n       \"      <td>-155</td>\\n\",\n       \"      <td>-652</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>3545.357143</td>\\n\",\n       \"      <td>10822.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>24817.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>100002</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-874.000000</td>\\n\",\n       \"      <td>-103</td>\\n\",\n       \"      <td>-1437</td>\\n\",\n       \"      <td>-6992</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-499.875000</td>\\n\",\n       \"      <td>-7</td>\\n\",\n       \"      <td>-1185</td>\\n\",\n       \"      <td>-3999</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>100003</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-1400.750000</td>\\n\",\n       \"      <td>-606</td>\\n\",\n       \"      <td>-2586</td>\\n\",\n       \"      <td>-5603</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-816.000000</td>\\n\",\n       \"      <td>-43</td>\\n\",\n       \"      <td>-2131</td>\\n\",\n       \"      <td>-3264</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>100004</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>-867.000000</td>\\n\",\n       \"      <td>-408</td>\\n\",\n       \"      <td>-1326</td>\\n\",\n       \"      <td>-1734</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>-532.000000</td>\\n\",\n       \"      <td>-382</td>\\n\",\n       \"      <td>-682</td>\\n\",\n       \"      <td>-1064</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>100005</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>-190.666667</td>\\n\",\n       \"      <td>-62</td>\\n\",\n       \"      <td>-373</td>\\n\",\n       \"      <td>-572</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>-54.333333</td>\\n\",\n       \"      <td>-11</td>\\n\",\n       \"      <td>-121</td>\\n\",\n       \"      <td>-163</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1420.500000</td>\\n\",\n       \"      <td>4261.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4261.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 61 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"  SK_ID_CURR DAYS_CREDIT                               CREDIT_DAY_OVERDUE  \\\\\\n\",\n       \"                   count         mean  max   min   sum              count   \\n\",\n       \"0     100001           7  -735.000000  -49 -1572 -5145                  7   \\n\",\n       \"1     100002           8  -874.000000 -103 -1437 -6992                  8   \\n\",\n       \"2     100003           4 -1400.750000 -606 -2586 -5603                  4   \\n\",\n       \"3     100004           2  -867.000000 -408 -1326 -1734                  2   \\n\",\n       \"4     100005           3  -190.666667  -62  -373  -572                  3   \\n\",\n       \"\\n\",\n       \"                 ...    DAYS_CREDIT_UPDATE                               \\\\\\n\",\n       \"  mean max min   ...                 count        mean  max   min   sum   \\n\",\n       \"0  0.0   0   0   ...                     7  -93.142857   -6  -155  -652   \\n\",\n       \"1  0.0   0   0   ...                     8 -499.875000   -7 -1185 -3999   \\n\",\n       \"2  0.0   0   0   ...                     4 -816.000000  -43 -2131 -3264   \\n\",\n       \"3  0.0   0   0   ...                     2 -532.000000 -382  -682 -1064   \\n\",\n       \"4  0.0   0   0   ...                     3  -54.333333  -11  -121  -163   \\n\",\n       \"\\n\",\n       \"  AMT_ANNUITY                                      \\n\",\n       \"        count         mean      max  min      sum  \\n\",\n       \"0           7  3545.357143  10822.5  0.0  24817.5  \\n\",\n       \"1           7     0.000000      0.0  0.0      0.0  \\n\",\n       \"2           0          NaN      NaN  NaN      0.0  \\n\",\n       \"3           0          NaN      NaN  NaN      0.0  \\n\",\n       \"4           3  1420.500000   4261.5  0.0   4261.5  \\n\",\n       \"\\n\",\n       \"[5 rows x 61 columns]\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 按照用户id分组并聚合数据\\n\",\n    \"bureau_agg = bureau.drop(columns = ['SK_ID_BUREAU']).groupby('SK_ID_CURR', as_index = False).agg(['count', 'mean', 'max', 'min', 'sum']).reset_index()\\n\",\n    \"bureau_agg.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们需要为每一列创建新的名称。下面的代码通过结合bureau字段生成新名称。但是利用上述方法生成数据文件具有多级索引，我发现这会让这些数据变得难以理解和处理，所以我想尽快将其削减为单级结构\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 列表的列名称\\n\",\n    \"columns = ['SK_ID_CURR']\\n\",\n    \"\\n\",\n    \"# 根据变量名迭代\\n\",\n    \"for var in bureau_agg.columns.levels[0]:\\n\",\n    \"    # 跳过id名（因为id名的mean、max...统计量没有任何意义）\\n\",\n    \"    if var != 'SK_ID_CURR':\\n\",\n    \"    \\n\",\n    \"        #通过结合bureau字段生成新名称\\n\",\n    \"        for stat in bureau_agg.columns.levels[1][:-1]:# 遍历第二行的所有列\\n\",\n    \"            columns.append('bureau_%s_%s' % (var, stat))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_count</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_mean</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_max</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_min</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_sum</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_count</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_mean</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_max</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_min</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_count</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_min</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_sum</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_count</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_mean</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_max</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_min</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_sum</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>100001</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-735.000000</td>\\n\",\n       \"      <td>-49</td>\\n\",\n       \"      <td>-1572</td>\\n\",\n       \"      <td>-5145</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-93.142857</td>\\n\",\n       \"      <td>-6</td>\\n\",\n       \"      <td>-155</td>\\n\",\n       \"      <td>-652</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>3545.357143</td>\\n\",\n       \"      <td>10822.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>24817.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>100002</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-874.000000</td>\\n\",\n       \"      <td>-103</td>\\n\",\n       \"      <td>-1437</td>\\n\",\n       \"      <td>-6992</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-499.875000</td>\\n\",\n       \"      <td>-7</td>\\n\",\n       \"      <td>-1185</td>\\n\",\n       \"      <td>-3999</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>100003</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-1400.750000</td>\\n\",\n       \"      <td>-606</td>\\n\",\n       \"      <td>-2586</td>\\n\",\n       \"      <td>-5603</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-816.000000</td>\\n\",\n       \"      <td>-43</td>\\n\",\n       \"      <td>-2131</td>\\n\",\n       \"      <td>-3264</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>100004</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>-867.000000</td>\\n\",\n       \"      <td>-408</td>\\n\",\n       \"      <td>-1326</td>\\n\",\n       \"      <td>-1734</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>-532.000000</td>\\n\",\n       \"      <td>-382</td>\\n\",\n       \"      <td>-682</td>\\n\",\n       \"      <td>-1064</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>100005</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>-190.666667</td>\\n\",\n       \"      <td>-62</td>\\n\",\n       \"      <td>-373</td>\\n\",\n       \"      <td>-572</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>-54.333333</td>\\n\",\n       \"      <td>-11</td>\\n\",\n       \"      <td>-121</td>\\n\",\n       \"      <td>-163</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1420.500000</td>\\n\",\n       \"      <td>4261.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4261.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 61 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_CURR  bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  \\\\\\n\",\n       \"0      100001                         7              -735.000000   \\n\",\n       \"1      100002                         8              -874.000000   \\n\",\n       \"2      100003                         4             -1400.750000   \\n\",\n       \"3      100004                         2              -867.000000   \\n\",\n       \"4      100005                         3              -190.666667   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_max  bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\\\\n\",\n       \"0                     -49                   -1572                   -5145   \\n\",\n       \"1                    -103                   -1437                   -6992   \\n\",\n       \"2                    -606                   -2586                   -5603   \\n\",\n       \"3                    -408                   -1326                   -1734   \\n\",\n       \"4                     -62                    -373                    -572   \\n\",\n       \"\\n\",\n       \"   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\\\\n\",\n       \"0                                7                             0.0   \\n\",\n       \"1                                8                             0.0   \\n\",\n       \"2                                4                             0.0   \\n\",\n       \"3                                2                             0.0   \\n\",\n       \"4                                3                             0.0   \\n\",\n       \"\\n\",\n       \"   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  \\\\\\n\",\n       \"0                              0                              0   \\n\",\n       \"1                              0                              0   \\n\",\n       \"2                              0                              0   \\n\",\n       \"3                              0                              0   \\n\",\n       \"4                              0                              0   \\n\",\n       \"\\n\",\n       \"            ...            bureau_DAYS_CREDIT_UPDATE_count  \\\\\\n\",\n       \"0           ...                                          7   \\n\",\n       \"1           ...                                          8   \\n\",\n       \"2           ...                                          4   \\n\",\n       \"3           ...                                          2   \\n\",\n       \"4           ...                                          3   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_UPDATE_mean  bureau_DAYS_CREDIT_UPDATE_max  \\\\\\n\",\n       \"0                      -93.142857                             -6   \\n\",\n       \"1                     -499.875000                             -7   \\n\",\n       \"2                     -816.000000                            -43   \\n\",\n       \"3                     -532.000000                           -382   \\n\",\n       \"4                      -54.333333                            -11   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_UPDATE_min  bureau_DAYS_CREDIT_UPDATE_sum  \\\\\\n\",\n       \"0                           -155                           -652   \\n\",\n       \"1                          -1185                          -3999   \\n\",\n       \"2                          -2131                          -3264   \\n\",\n       \"3                           -682                          -1064   \\n\",\n       \"4                           -121                           -163   \\n\",\n       \"\\n\",\n       \"   bureau_AMT_ANNUITY_count  bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  \\\\\\n\",\n       \"0                         7              3545.357143                 10822.5   \\n\",\n       \"1                         7                 0.000000                     0.0   \\n\",\n       \"2                         0                      NaN                     NaN   \\n\",\n       \"3                         0                      NaN                     NaN   \\n\",\n       \"4                         3              1420.500000                  4261.5   \\n\",\n       \"\\n\",\n       \"   bureau_AMT_ANNUITY_min  bureau_AMT_ANNUITY_sum  \\n\",\n       \"0                     0.0                 24817.5  \\n\",\n       \"1                     0.0                     0.0  \\n\",\n       \"2                     NaN                     0.0  \\n\",\n       \"3                     NaN                     0.0  \\n\",\n       \"4                     0.0                  4261.5  \\n\",\n       \"\\n\",\n       \"[5 rows x 61 columns]\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 将生成的类名列表作为新的类名\\n\",\n    \"bureau_agg.columns = columns\\n\",\n    \"bureau_agg.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在我们只需要简单地将之前处理好的数据与训练数据结合在一起\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th>TARGET</th>\\n\",\n       \"      <th>NAME_CONTRACT_TYPE</th>\\n\",\n       \"      <th>CODE_GENDER</th>\\n\",\n       \"      <th>FLAG_OWN_CAR</th>\\n\",\n       \"      <th>FLAG_OWN_REALTY</th>\\n\",\n       \"      <th>CNT_CHILDREN</th>\\n\",\n       \"      <th>AMT_INCOME_TOTAL</th>\\n\",\n       \"      <th>AMT_CREDIT</th>\\n\",\n       \"      <th>AMT_ANNUITY</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_count</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_min</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_sum</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_count</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_mean</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_max</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_min</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_sum</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>100002</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>202500.0</td>\\n\",\n       \"      <td>406597.5</td>\\n\",\n       \"      <td>24700.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>8.0</td>\\n\",\n       \"      <td>-499.875</td>\\n\",\n       \"      <td>-7.0</td>\\n\",\n       \"      <td>-1185.0</td>\\n\",\n       \"      <td>-3999.0</td>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>100003</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>270000.0</td>\\n\",\n       \"      <td>1293502.5</td>\\n\",\n       \"      <td>35698.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>-816.000</td>\\n\",\n       \"      <td>-43.0</td>\\n\",\n       \"      <td>-2131.0</td>\\n\",\n       \"      <td>-3264.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>100004</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Revolving loans</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>67500.0</td>\\n\",\n       \"      <td>135000.0</td>\\n\",\n       \"      <td>6750.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>-532.000</td>\\n\",\n       \"      <td>-382.0</td>\\n\",\n       \"      <td>-682.0</td>\\n\",\n       \"      <td>-1064.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>100006</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>135000.0</td>\\n\",\n       \"      <td>312682.5</td>\\n\",\n       \"      <td>29686.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>100007</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>121500.0</td>\\n\",\n       \"      <td>513000.0</td>\\n\",\n       \"      <td>21865.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>-783.000</td>\\n\",\n       \"      <td>-783.0</td>\\n\",\n       \"      <td>-783.0</td>\\n\",\n       \"      <td>-783.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 183 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\\\\n\",\n       \"0      100002       1         Cash loans           M            N   \\n\",\n       \"1      100003       0         Cash loans           F            N   \\n\",\n       \"2      100004       0    Revolving loans           M            Y   \\n\",\n       \"3      100006       0         Cash loans           F            N   \\n\",\n       \"4      100007       0         Cash loans           M            N   \\n\",\n       \"\\n\",\n       \"  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\\\\n\",\n       \"0               Y             0          202500.0    406597.5      24700.5   \\n\",\n       \"1               N             0          270000.0   1293502.5      35698.5   \\n\",\n       \"2               Y             0           67500.0    135000.0       6750.0   \\n\",\n       \"3               Y             0          135000.0    312682.5      29686.5   \\n\",\n       \"4               Y             0          121500.0    513000.0      21865.5   \\n\",\n       \"\\n\",\n       \"            ...            bureau_DAYS_CREDIT_UPDATE_count  \\\\\\n\",\n       \"0           ...                                        8.0   \\n\",\n       \"1           ...                                        4.0   \\n\",\n       \"2           ...                                        2.0   \\n\",\n       \"3           ...                                        NaN   \\n\",\n       \"4           ...                                        1.0   \\n\",\n       \"\\n\",\n       \"  bureau_DAYS_CREDIT_UPDATE_mean bureau_DAYS_CREDIT_UPDATE_max  \\\\\\n\",\n       \"0                       -499.875                          -7.0   \\n\",\n       \"1                       -816.000                         -43.0   \\n\",\n       \"2                       -532.000                        -382.0   \\n\",\n       \"3                            NaN                           NaN   \\n\",\n       \"4                       -783.000                        -783.0   \\n\",\n       \"\\n\",\n       \"  bureau_DAYS_CREDIT_UPDATE_min bureau_DAYS_CREDIT_UPDATE_sum  \\\\\\n\",\n       \"0                       -1185.0                       -3999.0   \\n\",\n       \"1                       -2131.0                       -3264.0   \\n\",\n       \"2                        -682.0                       -1064.0   \\n\",\n       \"3                           NaN                           NaN   \\n\",\n       \"4                        -783.0                        -783.0   \\n\",\n       \"\\n\",\n       \"  bureau_AMT_ANNUITY_count  bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  \\\\\\n\",\n       \"0                      7.0                      0.0                     0.0   \\n\",\n       \"1                      0.0                      NaN                     NaN   \\n\",\n       \"2                      0.0                      NaN                     NaN   \\n\",\n       \"3                      NaN                      NaN                     NaN   \\n\",\n       \"4                      0.0                      NaN                     NaN   \\n\",\n       \"\\n\",\n       \"   bureau_AMT_ANNUITY_min  bureau_AMT_ANNUITY_sum  \\n\",\n       \"0                     0.0                     0.0  \\n\",\n       \"1                     NaN                     0.0  \\n\",\n       \"2                     NaN                     0.0  \\n\",\n       \"3                     NaN                     NaN  \\n\",\n       \"4                     NaN                     0.0  \\n\",\n       \"\\n\",\n       \"[5 rows x 183 columns]\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 与训练数据结合\\n\",\n    \"train = train.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')\\n\",\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 聚合值和目标的相关性\\n\",\n    \"我们可以计算新的变量与目标值之间的相关性，同样的，我们可以用该相关系数作为变量对于模型重要性的一个估计\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 相关系数列表\\n\",\n    \"new_corrs = []\\n\",\n    \"\\n\",\n    \"# 根据变量名迭代\\n\",\n    \"for col in columns:\\n\",\n    \"    # 计算与目标值之间的相关系数\\n\",\n    \"    corr = train['TARGET'].corr(train[col])\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    # 以tuple的形式存入list中\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    new_corrs.append((col, corr))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在下面的代码中，我们使用Python中的排序函数来对相关系数按照绝对值的大小进行排序。同时，我们还使用了Python中的另一个重要操作——匿名函数lambda\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[('bureau_DAYS_CREDIT_mean', 0.089728967219981),\\n\",\n       \" ('bureau_DAYS_CREDIT_min', 0.07524825103010374),\\n\",\n       \" ('bureau_DAYS_CREDIT_UPDATE_mean', 0.06892735266968684),\\n\",\n       \" ('bureau_DAYS_ENDDATE_FACT_min', 0.055887379843920795),\\n\",\n       \" ('bureau_DAYS_CREDIT_ENDDATE_sum', 0.053734895601020585),\\n\",\n       \" ('bureau_DAYS_ENDDATE_FACT_mean', 0.05319962585758622),\\n\",\n       \" ('bureau_DAYS_CREDIT_max', 0.04978205463997309),\\n\",\n       \" ('bureau_DAYS_ENDDATE_FACT_sum', 0.048853502611115936),\\n\",\n       \" ('bureau_DAYS_CREDIT_ENDDATE_mean', 0.04698275433483553),\\n\",\n       \" ('bureau_DAYS_CREDIT_UPDATE_min', 0.04286392247073023),\\n\",\n       \" ('bureau_DAYS_CREDIT_sum', 0.04199982481484667),\\n\",\n       \" ('bureau_DAYS_CREDIT_UPDATE_sum', 0.0414036353530601),\\n\",\n       \" ('bureau_DAYS_CREDIT_ENDDATE_max', 0.03658963469632898),\\n\",\n       \" ('bureau_DAYS_CREDIT_ENDDATE_min', 0.034281109921615996),\\n\",\n       \" ('bureau_DAYS_ENDDATE_FACT_count', -0.03049230665332547)]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 对相关系数按照绝对值的大小进行排序\\n\",\n    \"# 使用反转操作，确保最大值在list的最前面\\n\",\n    \"new_corrs = sorted(new_corrs, key = lambda x: abs(x[1]), reverse = True)\\n\",\n    \"new_corrs[:15] # 输出前15个数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"没有一个新变量与目标值TARGET有显著的相关性。我们可以从KDE图中看出，最高相关性的向量——bureau_DAYS_CREDIT_mean与目标值TARGET在绝对值方面的相关性\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The correlation between bureau_DAYS_CREDIT_mean and the TARGET is 0.0897\\n\",\n      \"Median value for loan that was not repaid = -835.3333\\n\",\n      \"Median value for loan that was repaid =     -1067.0000\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x177000935f8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"kde_target('bureau_DAYS_CREDIT_mean', train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这一列的定义是：客户前一次申请贷款距离现在的天数。我的解释是：这是在此次申请捷信公司贷款之前，距离上一次向其他金融机构申请贷款中间所隔的天数。因此，负数的绝对值越大，意味着前一次的贷款越久远(-835.333/-1067)。我们看到，这个变量的平均值与目标之间存在着极弱的正相关关系，这意味着更早之前申请贷款的客户更有可能向Home Credit偿还贷款（不违约），尽管这种微弱的相关性很可能是噪声\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 多重比较问题\\n\",\n    \"让我们将之前的工作封装成一个函数，这样就可以在任何数值型数据中使用该方法。当我们想要对其他数据进行同样的操作时，只需要重用该函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def agg_numeric(df, group_var, df_name):\\n\",\n    \"    \\\"\\\"\\\"聚合数值型数据，可以用于为每个分组变量的实例创建新特征\\n\",\n    \"    \\n\",\n    \"    参数\\n\",\n    \"    --------\\n\",\n    \"        df (dataframe): \\n\",\n    \"            数据文件\\n\",\n    \"        group_var (string): \\n\",\n    \"            用于将数据文件分组的变量\\n\",\n    \"        df_name (string): \\n\",\n    \"            用于重命名列名的变量\\n\",\n    \"            \\n\",\n    \"    返回值\\n\",\n    \"    --------\\n\",\n    \"        agg (dataframe): \\n\",\n    \"            包含所有数值列聚合后统计量的数据文件，分组变量的每个实例都将计算出相应的统计量（平均值、最小值、最大值和总和）\\n\",\n    \"            为了便于追踪所创建这些新变量，这些数值列同时会被重命名  \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # 除去分组变量以外的id变量\\n\",\n    \"    for col in df:\\n\",\n    \"        if col != group_var and 'SK_ID' in col:\\n\",\n    \"            df = df.drop(columns = col)\\n\",\n    \"            \\n\",\n    \"    group_ids = df[group_var]\\n\",\n    \"    numeric_df = df.select_dtypes('number') # 选择数值列【df只剩下id列和需要分组的列？】\\n\",\n    \"    numeric_df[group_var] = group_ids\\n\",\n    \"    \\n\",\n    \"    # 通过指定变量分组并计算统计数据\\n\",\n    \"    agg = numeric_df.groupby(group_var).agg(['count', 'mean', 'max', 'min', 'sum']).reset_index()\\n\",\n    \"    \\n\",\n    \"    # 需要新建一个列名\\n\",\n    \"    columns = [group_var]\\n\",\n    \"    \\n\",\n    \"    # 根据变量名迭代\\n\",\n    \"    for var in agg.columns.levels[0]:\\n\",\n    \"        # 跳过分组变量\\n\",\n    \"        if var != group_var:\\n\",\n    \"            #通过结合相关字段生成新名称\\n\",\n    \"            for stat in agg.columns.levels[1][:-1]:\\n\",\n    \"                columns.append('%s_%s_%s' % (df_name, var, stat))\\n\",\n    \"    \\n\",\n    \"    agg.columns = columns\\n\",\n    \"    return agg\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_count</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_mean</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_max</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_min</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_sum</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_count</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_mean</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_max</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_min</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_count</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_min</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_sum</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_count</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_mean</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_max</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_min</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_sum</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>100001</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-735.000000</td>\\n\",\n       \"      <td>-49</td>\\n\",\n       \"      <td>-1572</td>\\n\",\n       \"      <td>-5145</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-93.142857</td>\\n\",\n       \"      <td>-6</td>\\n\",\n       \"      <td>-155</td>\\n\",\n       \"      <td>-652</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>3545.357143</td>\\n\",\n       \"      <td>10822.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>24817.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>100002</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-874.000000</td>\\n\",\n       \"      <td>-103</td>\\n\",\n       \"      <td>-1437</td>\\n\",\n       \"      <td>-6992</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-499.875000</td>\\n\",\n       \"      <td>-7</td>\\n\",\n       \"      <td>-1185</td>\\n\",\n       \"      <td>-3999</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>100003</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-1400.750000</td>\\n\",\n       \"      <td>-606</td>\\n\",\n       \"      <td>-2586</td>\\n\",\n       \"      <td>-5603</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-816.000000</td>\\n\",\n       \"      <td>-43</td>\\n\",\n       \"      <td>-2131</td>\\n\",\n       \"      <td>-3264</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>100004</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>-867.000000</td>\\n\",\n       \"      <td>-408</td>\\n\",\n       \"      <td>-1326</td>\\n\",\n       \"      <td>-1734</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>-532.000000</td>\\n\",\n       \"      <td>-382</td>\\n\",\n       \"      <td>-682</td>\\n\",\n       \"      <td>-1064</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>100005</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>-190.666667</td>\\n\",\n       \"      <td>-62</td>\\n\",\n       \"      <td>-373</td>\\n\",\n       \"      <td>-572</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>-54.333333</td>\\n\",\n       \"      <td>-11</td>\\n\",\n       \"      <td>-121</td>\\n\",\n       \"      <td>-163</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1420.500000</td>\\n\",\n       \"      <td>4261.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4261.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 61 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_CURR  bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  \\\\\\n\",\n       \"0      100001                         7              -735.000000   \\n\",\n       \"1      100002                         8              -874.000000   \\n\",\n       \"2      100003                         4             -1400.750000   \\n\",\n       \"3      100004                         2              -867.000000   \\n\",\n       \"4      100005                         3              -190.666667   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_max  bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\\\\n\",\n       \"0                     -49                   -1572                   -5145   \\n\",\n       \"1                    -103                   -1437                   -6992   \\n\",\n       \"2                    -606                   -2586                   -5603   \\n\",\n       \"3                    -408                   -1326                   -1734   \\n\",\n       \"4                     -62                    -373                    -572   \\n\",\n       \"\\n\",\n       \"   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\\\\n\",\n       \"0                                7                             0.0   \\n\",\n       \"1                                8                             0.0   \\n\",\n       \"2                                4                             0.0   \\n\",\n       \"3                                2                             0.0   \\n\",\n       \"4                                3                             0.0   \\n\",\n       \"\\n\",\n       \"   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  \\\\\\n\",\n       \"0                              0                              0   \\n\",\n       \"1                              0                              0   \\n\",\n       \"2                              0                              0   \\n\",\n       \"3                              0                              0   \\n\",\n       \"4                              0                              0   \\n\",\n       \"\\n\",\n       \"            ...            bureau_DAYS_CREDIT_UPDATE_count  \\\\\\n\",\n       \"0           ...                                          7   \\n\",\n       \"1           ...                                          8   \\n\",\n       \"2           ...                                          4   \\n\",\n       \"3           ...                                          2   \\n\",\n       \"4           ...                                          3   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_UPDATE_mean  bureau_DAYS_CREDIT_UPDATE_max  \\\\\\n\",\n       \"0                      -93.142857                             -6   \\n\",\n       \"1                     -499.875000                             -7   \\n\",\n       \"2                     -816.000000                            -43   \\n\",\n       \"3                     -532.000000                           -382   \\n\",\n       \"4                      -54.333333                            -11   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_UPDATE_min  bureau_DAYS_CREDIT_UPDATE_sum  \\\\\\n\",\n       \"0                           -155                           -652   \\n\",\n       \"1                          -1185                          -3999   \\n\",\n       \"2                          -2131                          -3264   \\n\",\n       \"3                           -682                          -1064   \\n\",\n       \"4                           -121                           -163   \\n\",\n       \"\\n\",\n       \"   bureau_AMT_ANNUITY_count  bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  \\\\\\n\",\n       \"0                         7              3545.357143                 10822.5   \\n\",\n       \"1                         7                 0.000000                     0.0   \\n\",\n       \"2                         0                      NaN                     NaN   \\n\",\n       \"3                         0                      NaN                     NaN   \\n\",\n       \"4                         3              1420.500000                  4261.5   \\n\",\n       \"\\n\",\n       \"   bureau_AMT_ANNUITY_min  bureau_AMT_ANNUITY_sum  \\n\",\n       \"0                     0.0                 24817.5  \\n\",\n       \"1                     0.0                     0.0  \\n\",\n       \"2                     NaN                     0.0  \\n\",\n       \"3                     NaN                     0.0  \\n\",\n       \"4                     0.0                  4261.5  \\n\",\n       \"\\n\",\n       \"[5 rows x 61 columns]\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bureau_agg_new = agg_numeric(bureau.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'bureau')\\n\",\n    \"bureau_agg_new.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"为了保证该函数按照预期的效果执行，我们需要与手工创建的聚合数据做对比\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_count</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_mean</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_max</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_min</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_sum</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_count</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_mean</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_max</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_min</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_count</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_min</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_sum</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_count</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_mean</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_max</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_min</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_sum</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>100001</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-735.000000</td>\\n\",\n       \"      <td>-49</td>\\n\",\n       \"      <td>-1572</td>\\n\",\n       \"      <td>-5145</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-93.142857</td>\\n\",\n       \"      <td>-6</td>\\n\",\n       \"      <td>-155</td>\\n\",\n       \"      <td>-652</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>3545.357143</td>\\n\",\n       \"      <td>10822.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>24817.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>100002</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-874.000000</td>\\n\",\n       \"      <td>-103</td>\\n\",\n       \"      <td>-1437</td>\\n\",\n       \"      <td>-6992</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-499.875000</td>\\n\",\n       \"      <td>-7</td>\\n\",\n       \"      <td>-1185</td>\\n\",\n       \"      <td>-3999</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>100003</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-1400.750000</td>\\n\",\n       \"      <td>-606</td>\\n\",\n       \"      <td>-2586</td>\\n\",\n       \"      <td>-5603</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-816.000000</td>\\n\",\n       \"      <td>-43</td>\\n\",\n       \"      <td>-2131</td>\\n\",\n       \"      <td>-3264</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>100004</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>-867.000000</td>\\n\",\n       \"      <td>-408</td>\\n\",\n       \"      <td>-1326</td>\\n\",\n       \"      <td>-1734</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>-532.000000</td>\\n\",\n       \"      <td>-382</td>\\n\",\n       \"      <td>-682</td>\\n\",\n       \"      <td>-1064</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>100005</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>-190.666667</td>\\n\",\n       \"      <td>-62</td>\\n\",\n       \"      <td>-373</td>\\n\",\n       \"      <td>-572</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>-54.333333</td>\\n\",\n       \"      <td>-11</td>\\n\",\n       \"      <td>-121</td>\\n\",\n       \"      <td>-163</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1420.500000</td>\\n\",\n       \"      <td>4261.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4261.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 61 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_CURR  bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  \\\\\\n\",\n       \"0      100001                         7              -735.000000   \\n\",\n       \"1      100002                         8              -874.000000   \\n\",\n       \"2      100003                         4             -1400.750000   \\n\",\n       \"3      100004                         2              -867.000000   \\n\",\n       \"4      100005                         3              -190.666667   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_max  bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\\\\n\",\n       \"0                     -49                   -1572                   -5145   \\n\",\n       \"1                    -103                   -1437                   -6992   \\n\",\n       \"2                    -606                   -2586                   -5603   \\n\",\n       \"3                    -408                   -1326                   -1734   \\n\",\n       \"4                     -62                    -373                    -572   \\n\",\n       \"\\n\",\n       \"   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\\\\n\",\n       \"0                                7                             0.0   \\n\",\n       \"1                                8                             0.0   \\n\",\n       \"2                                4                             0.0   \\n\",\n       \"3                                2                             0.0   \\n\",\n       \"4                                3                             0.0   \\n\",\n       \"\\n\",\n       \"   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  \\\\\\n\",\n       \"0                              0                              0   \\n\",\n       \"1                              0                              0   \\n\",\n       \"2                              0                              0   \\n\",\n       \"3                              0                              0   \\n\",\n       \"4                              0                              0   \\n\",\n       \"\\n\",\n       \"            ...            bureau_DAYS_CREDIT_UPDATE_count  \\\\\\n\",\n       \"0           ...                                          7   \\n\",\n       \"1           ...                                          8   \\n\",\n       \"2           ...                                          4   \\n\",\n       \"3           ...                                          2   \\n\",\n       \"4           ...                                          3   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_UPDATE_mean  bureau_DAYS_CREDIT_UPDATE_max  \\\\\\n\",\n       \"0                      -93.142857                             -6   \\n\",\n       \"1                     -499.875000                             -7   \\n\",\n       \"2                     -816.000000                            -43   \\n\",\n       \"3                     -532.000000                           -382   \\n\",\n       \"4                      -54.333333                            -11   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_UPDATE_min  bureau_DAYS_CREDIT_UPDATE_sum  \\\\\\n\",\n       \"0                           -155                           -652   \\n\",\n       \"1                          -1185                          -3999   \\n\",\n       \"2                          -2131                          -3264   \\n\",\n       \"3                           -682                          -1064   \\n\",\n       \"4                           -121                           -163   \\n\",\n       \"\\n\",\n       \"   bureau_AMT_ANNUITY_count  bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  \\\\\\n\",\n       \"0                         7              3545.357143                 10822.5   \\n\",\n       \"1                         7                 0.000000                     0.0   \\n\",\n       \"2                         0                      NaN                     NaN   \\n\",\n       \"3                         0                      NaN                     NaN   \\n\",\n       \"4                         3              1420.500000                  4261.5   \\n\",\n       \"\\n\",\n       \"   bureau_AMT_ANNUITY_min  bureau_AMT_ANNUITY_sum  \\n\",\n       \"0                     0.0                 24817.5  \\n\",\n       \"1                     0.0                     0.0  \\n\",\n       \"2                     NaN                     0.0  \\n\",\n       \"3                     NaN                     0.0  \\n\",\n       \"4                     0.0                  4261.5  \\n\",\n       \"\\n\",\n       \"[5 rows x 61 columns]\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bureau_agg.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"观察这些值可以发现它们是等价的，所以我们可以在其他数据集上重用该函数。通过使用函数减少工作量，是我们未来需要不断努力的方向\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 相关系数函数\\n\",\n    \"在继续下一步之前，我们同样可以封装计算变量与目标值之间相关系数的函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 计算数据集中变量与目标值之间相关系数的函数\\n\",\n    \"def target_corrs(df):\\n\",\n    \"    \\n\",\n    \"    # 相关系数数组\\n\",\n    \"    corrs = []\\n\",\n    \"    \\n\",\n    \"    # 按列迭代\\n\",\n    \"    for col in df.columns:\\n\",\n    \"        print(col)\\n\",\n    \"        # 跳过目标列\\n\",\n    \"        if col != 'TARGET':\\n\",\n    \"            # 计算相关系数\\n\",\n    \"            corr = df['TARGET'].corr(df[col])\\n\",\n    \"            \\n\",\n    \"            # 按tuple类型插入数组中\\n\",\n    \"            corrs.append((col, corr))\\n\",\n    \"    \\n\",\n    \"    # 按照相关系数绝对值大小排序\\n\",\n    \"    corrs = sorted(corrs, key = lambda x: abs(x[1]), reverse = True)\\n\",\n    \"    \\n\",\n    \"    return corrs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 类别变量(Categorical Variables)\\n\",\n    \"现在我们从数值列转向分类列。这些是离散型的字符串变量，所以我们无法计算均值或者最大值之类的统计量。相反，我们需要计算每个类别变量中类别的数量。举个例子，如果我们有以下的数据：\\n\",\n    \"SK_ID_CURR\\tLoan type\\n\",\n    \"1\\thome\\n\",\n    \"1\\thome\\n\",\n    \"1\\thome\\n\",\n    \"1\\tcredit\\n\",\n    \"2\\tcredit\\n\",\n    \"3\\tcredit\\n\",\n    \"3\\tcash\\n\",\n    \"3\\tcash\\n\",\n    \"4\\tcredit\\n\",\n    \"4\\thome\\n\",\n    \"4\\thome\\n\",\n    \"我们将使用这些信息，计算每个客户在每个类别的贷款数量\\n\",\n    \"SK_ID_CURR\\tcredit count\\tcash count\\thome count\\ttotal count\\n\",\n    \"1\\t1\\t0\\t3\\t4\\n\",\n    \"2\\t1\\t0\\t0\\t1\\n\",\n    \"3\\t1\\t2\\t0\\t3\\n\",\n    \"4\\t1\\t0\\t2\\t3\\n\",\n    \"然后，我们可以通过该分类变量出现的总数来标准化这些数据（这意味着每行规范化后的数据总和必须为1.0）\\n\",\n    \"SK_ID_CURR\\tcredit count\\tcash count\\thome count\\ttotal count\\tcredit count norm\\tcash count norm\\thome count norm\\n\",\n    \"1\\t1\\t0\\t3\\t4\\t0.25\\t0\\t0.75\\n\",\n    \"2\\t1\\t0\\t0\\t1\\t1.00\\t0\\t0\\n\",\n    \"3\\t1\\t2\\t0\\t3\\t0.33\\t0.66\\t0\\n\",\n    \"4\\t1\\t0\\t2\\t3\\t0.33\\t0\\t0.66\\n\",\n    \"希望用这种方式对类别变量进行编码可以获得它们所包含的信息。如果你们对这个过程有更好的想法，请在评论中告诉我！我们一步步进行这个过程，最后我将把所有的代码打包成一个函数，用于不同数据集的重用\\n\",\n    \"\\n\",\n    \"首先我们对分类列（dType＝=“object）的数据文件进行oen-hot编码\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>CREDIT_ACTIVE_Active</th>\\n\",\n       \"      <th>CREDIT_ACTIVE_Bad debt</th>\\n\",\n       \"      <th>CREDIT_ACTIVE_Closed</th>\\n\",\n       \"      <th>CREDIT_ACTIVE_Sold</th>\\n\",\n       \"      <th>CREDIT_CURRENCY_currency 1</th>\\n\",\n       \"      <th>CREDIT_CURRENCY_currency 2</th>\\n\",\n       \"      <th>CREDIT_CURRENCY_currency 3</th>\\n\",\n       \"      <th>CREDIT_CURRENCY_currency 4</th>\\n\",\n       \"      <th>CREDIT_TYPE_Another type of loan</th>\\n\",\n       \"      <th>CREDIT_TYPE_Car loan</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>CREDIT_TYPE_Loan for business development</th>\\n\",\n       \"      <th>CREDIT_TYPE_Loan for purchase of shares (margin lending)</th>\\n\",\n       \"      <th>CREDIT_TYPE_Loan for the purchase of equipment</th>\\n\",\n       \"      <th>CREDIT_TYPE_Loan for working capital replenishment</th>\\n\",\n       \"      <th>CREDIT_TYPE_Microloan</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mobile operator loan</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mortgage</th>\\n\",\n       \"      <th>CREDIT_TYPE_Real estate loan</th>\\n\",\n       \"      <th>CREDIT_TYPE_Unknown type of loan</th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>215354</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>215354</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>215354</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>215354</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>215354</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 24 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   CREDIT_ACTIVE_Active  CREDIT_ACTIVE_Bad debt  CREDIT_ACTIVE_Closed  \\\\\\n\",\n       \"0                     0                       0                     1   \\n\",\n       \"1                     1                       0                     0   \\n\",\n       \"2                     1                       0                     0   \\n\",\n       \"3                     1                       0                     0   \\n\",\n       \"4                     1                       0                     0   \\n\",\n       \"\\n\",\n       \"   CREDIT_ACTIVE_Sold  CREDIT_CURRENCY_currency 1  CREDIT_CURRENCY_currency 2  \\\\\\n\",\n       \"0                   0                           1                           0   \\n\",\n       \"1                   0                           1                           0   \\n\",\n       \"2                   0                           1                           0   \\n\",\n       \"3                   0                           1                           0   \\n\",\n       \"4                   0                           1                           0   \\n\",\n       \"\\n\",\n       \"   CREDIT_CURRENCY_currency 3  CREDIT_CURRENCY_currency 4  \\\\\\n\",\n       \"0                           0                           0   \\n\",\n       \"1                           0                           0   \\n\",\n       \"2                           0                           0   \\n\",\n       \"3                           0                           0   \\n\",\n       \"4                           0                           0   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Another type of loan  CREDIT_TYPE_Car loan     ...      \\\\\\n\",\n       \"0                                 0                     0     ...       \\n\",\n       \"1                                 0                     0     ...       \\n\",\n       \"2                                 0                     0     ...       \\n\",\n       \"3                                 0                     0     ...       \\n\",\n       \"4                                 0                     0     ...       \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Loan for business development  \\\\\\n\",\n       \"0                                          0   \\n\",\n       \"1                                          0   \\n\",\n       \"2                                          0   \\n\",\n       \"3                                          0   \\n\",\n       \"4                                          0   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Loan for purchase of shares (margin lending)  \\\\\\n\",\n       \"0                                                  0          \\n\",\n       \"1                                                  0          \\n\",\n       \"2                                                  0          \\n\",\n       \"3                                                  0          \\n\",\n       \"4                                                  0          \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Loan for the purchase of equipment  \\\\\\n\",\n       \"0                                               0   \\n\",\n       \"1                                               0   \\n\",\n       \"2                                               0   \\n\",\n       \"3                                               0   \\n\",\n       \"4                                               0   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Loan for working capital replenishment  CREDIT_TYPE_Microloan  \\\\\\n\",\n       \"0                                                  0                       0   \\n\",\n       \"1                                                  0                       0   \\n\",\n       \"2                                                  0                       0   \\n\",\n       \"3                                                  0                       0   \\n\",\n       \"4                                                  0                       0   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Mobile operator loan  CREDIT_TYPE_Mortgage  \\\\\\n\",\n       \"0                                 0                     0   \\n\",\n       \"1                                 0                     0   \\n\",\n       \"2                                 0                     0   \\n\",\n       \"3                                 0                     0   \\n\",\n       \"4                                 0                     0   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Real estate loan  CREDIT_TYPE_Unknown type of loan  SK_ID_CURR  \\n\",\n       \"0                             0                                 0      215354  \\n\",\n       \"1                             0                                 0      215354  \\n\",\n       \"2                             0                                 0      215354  \\n\",\n       \"3                             0                                 0      215354  \\n\",\n       \"4                             0                                 0      215354  \\n\",\n       \"\\n\",\n       \"[5 rows x 24 columns]\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 使用get_dummies进行one-hot编码，该方法会为列中的每个字符串创建一个新列\\n\",\n    \"categorical = pd.get_dummies(bureau.select_dtypes('object'))\\n\",\n    \"categorical['SK_ID_CURR'] = bureau['SK_ID_CURR']\\n\",\n    \"categorical.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead tr th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead tr:last-of-type th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th colspan=\\\"2\\\" halign=\\\"left\\\">CREDIT_ACTIVE_Active</th>\\n\",\n       \"      <th colspan=\\\"2\\\" halign=\\\"left\\\">CREDIT_ACTIVE_Bad debt</th>\\n\",\n       \"      <th colspan=\\\"2\\\" halign=\\\"left\\\">CREDIT_ACTIVE_Closed</th>\\n\",\n       \"      <th colspan=\\\"2\\\" halign=\\\"left\\\">CREDIT_ACTIVE_Sold</th>\\n\",\n       \"      <th colspan=\\\"2\\\" halign=\\\"left\\\">CREDIT_CURRENCY_currency 1</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th colspan=\\\"2\\\" halign=\\\"left\\\">CREDIT_TYPE_Microloan</th>\\n\",\n       \"      <th colspan=\\\"2\\\" halign=\\\"left\\\">CREDIT_TYPE_Mobile operator loan</th>\\n\",\n       \"      <th colspan=\\\"2\\\" halign=\\\"left\\\">CREDIT_TYPE_Mortgage</th>\\n\",\n       \"      <th colspan=\\\"2\\\" halign=\\\"left\\\">CREDIT_TYPE_Real estate loan</th>\\n\",\n       \"      <th colspan=\\\"2\\\" halign=\\\"left\\\">CREDIT_TYPE_Unknown type of loan</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>sum</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100001</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.428571</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.571429</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100002</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100003</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100004</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100005</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.666667</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.333333</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 46 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"           CREDIT_ACTIVE_Active           CREDIT_ACTIVE_Bad debt       \\\\\\n\",\n       \"                            sum      mean                    sum mean   \\n\",\n       \"SK_ID_CURR                                                              \\n\",\n       \"100001                        3  0.428571                      0  0.0   \\n\",\n       \"100002                        2  0.250000                      0  0.0   \\n\",\n       \"100003                        1  0.250000                      0  0.0   \\n\",\n       \"100004                        0  0.000000                      0  0.0   \\n\",\n       \"100005                        2  0.666667                      0  0.0   \\n\",\n       \"\\n\",\n       \"           CREDIT_ACTIVE_Closed           CREDIT_ACTIVE_Sold       \\\\\\n\",\n       \"                            sum      mean                sum mean   \\n\",\n       \"SK_ID_CURR                                                          \\n\",\n       \"100001                        4  0.571429                  0  0.0   \\n\",\n       \"100002                        6  0.750000                  0  0.0   \\n\",\n       \"100003                        3  0.750000                  0  0.0   \\n\",\n       \"100004                        2  1.000000                  0  0.0   \\n\",\n       \"100005                        1  0.333333                  0  0.0   \\n\",\n       \"\\n\",\n       \"           CREDIT_CURRENCY_currency 1      ...  CREDIT_TYPE_Microloan       \\\\\\n\",\n       \"                                  sum mean ...                    sum mean   \\n\",\n       \"SK_ID_CURR                                 ...                               \\n\",\n       \"100001                              7  1.0 ...                      0  0.0   \\n\",\n       \"100002                              8  1.0 ...                      0  0.0   \\n\",\n       \"100003                              4  1.0 ...                      0  0.0   \\n\",\n       \"100004                              2  1.0 ...                      0  0.0   \\n\",\n       \"100005                              3  1.0 ...                      0  0.0   \\n\",\n       \"\\n\",\n       \"           CREDIT_TYPE_Mobile operator loan      CREDIT_TYPE_Mortgage       \\\\\\n\",\n       \"                                        sum mean                  sum mean   \\n\",\n       \"SK_ID_CURR                                                                   \\n\",\n       \"100001                                    0  0.0                    0  0.0   \\n\",\n       \"100002                                    0  0.0                    0  0.0   \\n\",\n       \"100003                                    0  0.0                    0  0.0   \\n\",\n       \"100004                                    0  0.0                    0  0.0   \\n\",\n       \"100005                                    0  0.0                    0  0.0   \\n\",\n       \"\\n\",\n       \"           CREDIT_TYPE_Real estate loan      CREDIT_TYPE_Unknown type of loan  \\\\\\n\",\n       \"                                    sum mean                              sum   \\n\",\n       \"SK_ID_CURR                                                                      \\n\",\n       \"100001                                0  0.0                                0   \\n\",\n       \"100002                                0  0.0                                0   \\n\",\n       \"100003                                0  0.0                                0   \\n\",\n       \"100004                                0  0.0                                0   \\n\",\n       \"100005                                0  0.0                                0   \\n\",\n       \"\\n\",\n       \"                 \\n\",\n       \"           mean  \\n\",\n       \"SK_ID_CURR       \\n\",\n       \"100001      0.0  \\n\",\n       \"100002      0.0  \\n\",\n       \"100003      0.0  \\n\",\n       \"100004      0.0  \\n\",\n       \"100005      0.0  \\n\",\n       \"\\n\",\n       \"[5 rows x 46 columns]\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"categorical_grouped = categorical.groupby('SK_ID_CURR').agg(['sum', 'mean'])\\n\",\n    \"categorical_grouped.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"'sum'列表示的是与该类别相关联的客户总数，'mean'列表示的是其归一化计数。one-hot编码函数使得这些数值的计算变得十分容易\\n\",\n    \"\\n\",\n    \"我们可以像之前一样对列名进行重命名，同样的，我们需要处理列的多层索引问题。我们将第一级（0层）的分类变量名与one-hot编码后的值结合，迭代进行该过程。例如：sum列将会变成 CREDIT_ACTIVE_Active_count\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Index(['CREDIT_ACTIVE_Active', 'CREDIT_ACTIVE_Bad debt',\\n\",\n       \"       'CREDIT_ACTIVE_Closed', 'CREDIT_ACTIVE_Sold',\\n\",\n       \"       'CREDIT_CURRENCY_currency 1', 'CREDIT_CURRENCY_currency 2',\\n\",\n       \"       'CREDIT_CURRENCY_currency 3', 'CREDIT_CURRENCY_currency 4',\\n\",\n       \"       'CREDIT_TYPE_Another type of loan', 'CREDIT_TYPE_Car loan'],\\n\",\n       \"      dtype='object')\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"categorical_grouped.columns.levels[0][:10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Index(['sum', 'mean'], dtype='object')\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"categorical_grouped.columns.levels[1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>CREDIT_ACTIVE_Active_count</th>\\n\",\n       \"      <th>CREDIT_ACTIVE_Active_count_norm</th>\\n\",\n       \"      <th>CREDIT_ACTIVE_Bad debt_count</th>\\n\",\n       \"      <th>CREDIT_ACTIVE_Bad debt_count_norm</th>\\n\",\n       \"      <th>CREDIT_ACTIVE_Closed_count</th>\\n\",\n       \"      <th>CREDIT_ACTIVE_Closed_count_norm</th>\\n\",\n       \"      <th>CREDIT_ACTIVE_Sold_count</th>\\n\",\n       \"      <th>CREDIT_ACTIVE_Sold_count_norm</th>\\n\",\n       \"      <th>CREDIT_CURRENCY_currency 1_count</th>\\n\",\n       \"      <th>CREDIT_CURRENCY_currency 1_count_norm</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>CREDIT_TYPE_Microloan_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Microloan_count_norm</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mobile operator loan_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mobile operator loan_count_norm</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mortgage_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mortgage_count_norm</th>\\n\",\n       \"      <th>CREDIT_TYPE_Real estate loan_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Real estate loan_count_norm</th>\\n\",\n       \"      <th>CREDIT_TYPE_Unknown type of loan_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Unknown type of loan_count_norm</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100001</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.428571</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.571429</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100002</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100003</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100004</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100005</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.666667</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.333333</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 46 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"            CREDIT_ACTIVE_Active_count  CREDIT_ACTIVE_Active_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                                \\n\",\n       \"100001                               3                         0.428571   \\n\",\n       \"100002                               2                         0.250000   \\n\",\n       \"100003                               1                         0.250000   \\n\",\n       \"100004                               0                         0.000000   \\n\",\n       \"100005                               2                         0.666667   \\n\",\n       \"\\n\",\n       \"            CREDIT_ACTIVE_Bad debt_count  CREDIT_ACTIVE_Bad debt_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                                    \\n\",\n       \"100001                                 0                                0.0   \\n\",\n       \"100002                                 0                                0.0   \\n\",\n       \"100003                                 0                                0.0   \\n\",\n       \"100004                                 0                                0.0   \\n\",\n       \"100005                                 0                                0.0   \\n\",\n       \"\\n\",\n       \"            CREDIT_ACTIVE_Closed_count  CREDIT_ACTIVE_Closed_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                                \\n\",\n       \"100001                               4                         0.571429   \\n\",\n       \"100002                               6                         0.750000   \\n\",\n       \"100003                               3                         0.750000   \\n\",\n       \"100004                               2                         1.000000   \\n\",\n       \"100005                               1                         0.333333   \\n\",\n       \"\\n\",\n       \"            CREDIT_ACTIVE_Sold_count  CREDIT_ACTIVE_Sold_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                            \\n\",\n       \"100001                             0                            0.0   \\n\",\n       \"100002                             0                            0.0   \\n\",\n       \"100003                             0                            0.0   \\n\",\n       \"100004                             0                            0.0   \\n\",\n       \"100005                             0                            0.0   \\n\",\n       \"\\n\",\n       \"            CREDIT_CURRENCY_currency 1_count  \\\\\\n\",\n       \"SK_ID_CURR                                     \\n\",\n       \"100001                                     7   \\n\",\n       \"100002                                     8   \\n\",\n       \"100003                                     4   \\n\",\n       \"100004                                     2   \\n\",\n       \"100005                                     3   \\n\",\n       \"\\n\",\n       \"            CREDIT_CURRENCY_currency 1_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                          \\n\",\n       \"100001                                        1.0   \\n\",\n       \"100002                                        1.0   \\n\",\n       \"100003                                        1.0   \\n\",\n       \"100004                                        1.0   \\n\",\n       \"100005                                        1.0   \\n\",\n       \"\\n\",\n       \"                               ...                       \\\\\\n\",\n       \"SK_ID_CURR                     ...                        \\n\",\n       \"100001                         ...                        \\n\",\n       \"100002                         ...                        \\n\",\n       \"100003                         ...                        \\n\",\n       \"100004                         ...                        \\n\",\n       \"100005                         ...                        \\n\",\n       \"\\n\",\n       \"            CREDIT_TYPE_Microloan_count  CREDIT_TYPE_Microloan_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                                  \\n\",\n       \"100001                                0                               0.0   \\n\",\n       \"100002                                0                               0.0   \\n\",\n       \"100003                                0                               0.0   \\n\",\n       \"100004                                0                               0.0   \\n\",\n       \"100005                                0                               0.0   \\n\",\n       \"\\n\",\n       \"            CREDIT_TYPE_Mobile operator loan_count  \\\\\\n\",\n       \"SK_ID_CURR                                           \\n\",\n       \"100001                                           0   \\n\",\n       \"100002                                           0   \\n\",\n       \"100003                                           0   \\n\",\n       \"100004                                           0   \\n\",\n       \"100005                                           0   \\n\",\n       \"\\n\",\n       \"            CREDIT_TYPE_Mobile operator loan_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                \\n\",\n       \"100001                                              0.0   \\n\",\n       \"100002                                              0.0   \\n\",\n       \"100003                                              0.0   \\n\",\n       \"100004                                              0.0   \\n\",\n       \"100005                                              0.0   \\n\",\n       \"\\n\",\n       \"            CREDIT_TYPE_Mortgage_count  CREDIT_TYPE_Mortgage_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                                \\n\",\n       \"100001                               0                              0.0   \\n\",\n       \"100002                               0                              0.0   \\n\",\n       \"100003                               0                              0.0   \\n\",\n       \"100004                               0                              0.0   \\n\",\n       \"100005                               0                              0.0   \\n\",\n       \"\\n\",\n       \"            CREDIT_TYPE_Real estate loan_count  \\\\\\n\",\n       \"SK_ID_CURR                                       \\n\",\n       \"100001                                       0   \\n\",\n       \"100002                                       0   \\n\",\n       \"100003                                       0   \\n\",\n       \"100004                                       0   \\n\",\n       \"100005                                       0   \\n\",\n       \"\\n\",\n       \"            CREDIT_TYPE_Real estate loan_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                            \\n\",\n       \"100001                                          0.0   \\n\",\n       \"100002                                          0.0   \\n\",\n       \"100003                                          0.0   \\n\",\n       \"100004                                          0.0   \\n\",\n       \"100005                                          0.0   \\n\",\n       \"\\n\",\n       \"            CREDIT_TYPE_Unknown type of loan_count  \\\\\\n\",\n       \"SK_ID_CURR                                           \\n\",\n       \"100001                                           0   \\n\",\n       \"100002                                           0   \\n\",\n       \"100003                                           0   \\n\",\n       \"100004                                           0   \\n\",\n       \"100005                                           0   \\n\",\n       \"\\n\",\n       \"            CREDIT_TYPE_Unknown type of loan_count_norm  \\n\",\n       \"SK_ID_CURR                                               \\n\",\n       \"100001                                              0.0  \\n\",\n       \"100002                                              0.0  \\n\",\n       \"100003                                              0.0  \\n\",\n       \"100004                                              0.0  \\n\",\n       \"100005                                              0.0  \\n\",\n       \"\\n\",\n       \"[5 rows x 46 columns]\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"group_var = 'SK_ID_CURR'\\n\",\n    \"\\n\",\n    \"# 新建的列名\\n\",\n    \"columns = []\\n\",\n    \"\\n\",\n    \"# 迭代变量名\\n\",\n    \"for var in categorical_grouped.columns.levels[0]:\\n\",\n    \"    # 忽略分组变量\\n\",\n    \"    if var != group_var:\\n\",\n    \"        # 迭代统计量名称sum、mean\\n\",\n    \"        for stat in ['count', 'count_norm']:\\n\",\n    \"            columns.append('%s_%s' % (var, stat))\\n\",\n    \"\\n\",\n    \"# 重命名列\\n\",\n    \"categorical_grouped.columns = columns\\n\",\n    \"\\n\",\n    \"categorical_grouped.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"'sum'列记录了客户总数，'mean'列记录了归一化后的值\\n\",\n    \"\\n\",\n    \"我们可以将该数据合并到训练数据中\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th>TARGET</th>\\n\",\n       \"      <th>NAME_CONTRACT_TYPE</th>\\n\",\n       \"      <th>CODE_GENDER</th>\\n\",\n       \"      <th>FLAG_OWN_CAR</th>\\n\",\n       \"      <th>FLAG_OWN_REALTY</th>\\n\",\n       \"      <th>CNT_CHILDREN</th>\\n\",\n       \"      <th>AMT_INCOME_TOTAL</th>\\n\",\n       \"      <th>AMT_CREDIT</th>\\n\",\n       \"      <th>AMT_ANNUITY</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>CREDIT_TYPE_Microloan_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Microloan_count_norm</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mobile operator loan_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mobile operator loan_count_norm</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mortgage_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mortgage_count_norm</th>\\n\",\n       \"      <th>CREDIT_TYPE_Real estate loan_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Real estate loan_count_norm</th>\\n\",\n       \"      <th>CREDIT_TYPE_Unknown type of loan_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Unknown type of loan_count_norm</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>100002</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>202500.0</td>\\n\",\n       \"      <td>406597.5</td>\\n\",\n       \"      <td>24700.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>100003</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>270000.0</td>\\n\",\n       \"      <td>1293502.5</td>\\n\",\n       \"      <td>35698.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>100004</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Revolving loans</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>67500.0</td>\\n\",\n       \"      <td>135000.0</td>\\n\",\n       \"      <td>6750.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>100006</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>135000.0</td>\\n\",\n       \"      <td>312682.5</td>\\n\",\n       \"      <td>29686.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>100007</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>121500.0</td>\\n\",\n       \"      <td>513000.0</td>\\n\",\n       \"      <td>21865.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 229 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\\\\n\",\n       \"0      100002       1         Cash loans           M            N   \\n\",\n       \"1      100003       0         Cash loans           F            N   \\n\",\n       \"2      100004       0    Revolving loans           M            Y   \\n\",\n       \"3      100006       0         Cash loans           F            N   \\n\",\n       \"4      100007       0         Cash loans           M            N   \\n\",\n       \"\\n\",\n       \"  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\\\\n\",\n       \"0               Y             0          202500.0    406597.5      24700.5   \\n\",\n       \"1               N             0          270000.0   1293502.5      35698.5   \\n\",\n       \"2               Y             0           67500.0    135000.0       6750.0   \\n\",\n       \"3               Y             0          135000.0    312682.5      29686.5   \\n\",\n       \"4               Y             0          121500.0    513000.0      21865.5   \\n\",\n       \"\\n\",\n       \"                      ...                       CREDIT_TYPE_Microloan_count  \\\\\\n\",\n       \"0                     ...                                               0.0   \\n\",\n       \"1                     ...                                               0.0   \\n\",\n       \"2                     ...                                               0.0   \\n\",\n       \"3                     ...                                               NaN   \\n\",\n       \"4                     ...                                               0.0   \\n\",\n       \"\\n\",\n       \"  CREDIT_TYPE_Microloan_count_norm CREDIT_TYPE_Mobile operator loan_count  \\\\\\n\",\n       \"0                              0.0                                    0.0   \\n\",\n       \"1                              0.0                                    0.0   \\n\",\n       \"2                              0.0                                    0.0   \\n\",\n       \"3                              NaN                                    NaN   \\n\",\n       \"4                              0.0                                    0.0   \\n\",\n       \"\\n\",\n       \"  CREDIT_TYPE_Mobile operator loan_count_norm CREDIT_TYPE_Mortgage_count  \\\\\\n\",\n       \"0                                         0.0                        0.0   \\n\",\n       \"1                                         0.0                        0.0   \\n\",\n       \"2                                         0.0                        0.0   \\n\",\n       \"3                                         NaN                        NaN   \\n\",\n       \"4                                         0.0                        0.0   \\n\",\n       \"\\n\",\n       \"  CREDIT_TYPE_Mortgage_count_norm  CREDIT_TYPE_Real estate loan_count  \\\\\\n\",\n       \"0                             0.0                                 0.0   \\n\",\n       \"1                             0.0                                 0.0   \\n\",\n       \"2                             0.0                                 0.0   \\n\",\n       \"3                             NaN                                 NaN   \\n\",\n       \"4                             0.0                                 0.0   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Real estate loan_count_norm  \\\\\\n\",\n       \"0                                      0.0   \\n\",\n       \"1                                      0.0   \\n\",\n       \"2                                      0.0   \\n\",\n       \"3                                      NaN   \\n\",\n       \"4                                      0.0   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Unknown type of loan_count  \\\\\\n\",\n       \"0                                     0.0   \\n\",\n       \"1                                     0.0   \\n\",\n       \"2                                     0.0   \\n\",\n       \"3                                     NaN   \\n\",\n       \"4                                     0.0   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Unknown type of loan_count_norm  \\n\",\n       \"0                                          0.0  \\n\",\n       \"1                                          0.0  \\n\",\n       \"2                                          0.0  \\n\",\n       \"3                                          NaN  \\n\",\n       \"4                                          0.0  \\n\",\n       \"\\n\",\n       \"[5 rows x 229 columns]\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train = train.merge(categorical_grouped, left_on = 'SK_ID_CURR', right_index  =True, how = 'left')\\n\",\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(307511, 229)\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_count</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_mean</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_max</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_min</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_sum</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_count</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_mean</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_max</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_min</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_sum</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>CREDIT_TYPE_Microloan_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Microloan_count_norm</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mobile operator loan_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mobile operator loan_count_norm</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mortgage_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Mortgage_count_norm</th>\\n\",\n       \"      <th>CREDIT_TYPE_Real estate loan_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Real estate loan_count_norm</th>\\n\",\n       \"      <th>CREDIT_TYPE_Unknown type of loan_count</th>\\n\",\n       \"      <th>CREDIT_TYPE_Unknown type of loan_count_norm</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>8.0</td>\\n\",\n       \"      <td>-874.000000</td>\\n\",\n       \"      <td>-103.0</td>\\n\",\n       \"      <td>-1437.0</td>\\n\",\n       \"      <td>-6992.0</td>\\n\",\n       \"      <td>8.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>-1400.750000</td>\\n\",\n       \"      <td>-606.0</td>\\n\",\n       \"      <td>-2586.0</td>\\n\",\n       \"      <td>-5603.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>-867.000000</td>\\n\",\n       \"      <td>-408.0</td>\\n\",\n       \"      <td>-1326.0</td>\\n\",\n       \"      <td>-1734.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>-1149.000000</td>\\n\",\n       \"      <td>-1149.0</td>\\n\",\n       \"      <td>-1149.0</td>\\n\",\n       \"      <td>-1149.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>-757.333333</td>\\n\",\n       \"      <td>-78.0</td>\\n\",\n       \"      <td>-1097.0</td>\\n\",\n       \"      <td>-2272.0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>18.0</td>\\n\",\n       \"      <td>-1271.500000</td>\\n\",\n       \"      <td>-239.0</td>\\n\",\n       \"      <td>-2882.0</td>\\n\",\n       \"      <td>-22887.0</td>\\n\",\n       \"      <td>18.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>-1939.500000</td>\\n\",\n       \"      <td>-1138.0</td>\\n\",\n       \"      <td>-2741.0</td>\\n\",\n       \"      <td>-3879.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>-1773.000000</td>\\n\",\n       \"      <td>-1309.0</td>\\n\",\n       \"      <td>-2508.0</td>\\n\",\n       \"      <td>-7092.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>10 rows × 106 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  bureau_DAYS_CREDIT_max  \\\\\\n\",\n       \"0                       8.0              -874.000000                  -103.0   \\n\",\n       \"1                       4.0             -1400.750000                  -606.0   \\n\",\n       \"2                       2.0              -867.000000                  -408.0   \\n\",\n       \"3                       NaN                      NaN                     NaN   \\n\",\n       \"4                       1.0             -1149.000000                 -1149.0   \\n\",\n       \"5                       3.0              -757.333333                   -78.0   \\n\",\n       \"6                      18.0             -1271.500000                  -239.0   \\n\",\n       \"7                       2.0             -1939.500000                 -1138.0   \\n\",\n       \"8                       4.0             -1773.000000                 -1309.0   \\n\",\n       \"9                       NaN                      NaN                     NaN   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\\\\n\",\n       \"0                 -1437.0                 -6992.0   \\n\",\n       \"1                 -2586.0                 -5603.0   \\n\",\n       \"2                 -1326.0                 -1734.0   \\n\",\n       \"3                     NaN                     NaN   \\n\",\n       \"4                 -1149.0                 -1149.0   \\n\",\n       \"5                 -1097.0                 -2272.0   \\n\",\n       \"6                 -2882.0                -22887.0   \\n\",\n       \"7                 -2741.0                 -3879.0   \\n\",\n       \"8                 -2508.0                 -7092.0   \\n\",\n       \"9                     NaN                     NaN   \\n\",\n       \"\\n\",\n       \"   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\\\\n\",\n       \"0                              8.0                             0.0   \\n\",\n       \"1                              4.0                             0.0   \\n\",\n       \"2                              2.0                             0.0   \\n\",\n       \"3                              NaN                             NaN   \\n\",\n       \"4                              1.0                             0.0   \\n\",\n       \"5                              3.0                             0.0   \\n\",\n       \"6                             18.0                             0.0   \\n\",\n       \"7                              2.0                             0.0   \\n\",\n       \"8                              4.0                             0.0   \\n\",\n       \"9                              NaN                             NaN   \\n\",\n       \"\\n\",\n       \"   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  \\\\\\n\",\n       \"0                            0.0                            0.0   \\n\",\n       \"1                            0.0                            0.0   \\n\",\n       \"2                            0.0                            0.0   \\n\",\n       \"3                            NaN                            NaN   \\n\",\n       \"4                            0.0                            0.0   \\n\",\n       \"5                            0.0                            0.0   \\n\",\n       \"6                            0.0                            0.0   \\n\",\n       \"7                            0.0                            0.0   \\n\",\n       \"8                            0.0                            0.0   \\n\",\n       \"9                            NaN                            NaN   \\n\",\n       \"\\n\",\n       \"   bureau_CREDIT_DAY_OVERDUE_sum                     ...                       \\\\\\n\",\n       \"0                            0.0                     ...                        \\n\",\n       \"1                            0.0                     ...                        \\n\",\n       \"2                            0.0                     ...                        \\n\",\n       \"3                            NaN                     ...                        \\n\",\n       \"4                            0.0                     ...                        \\n\",\n       \"5                            0.0                     ...                        \\n\",\n       \"6                            0.0                     ...                        \\n\",\n       \"7                            0.0                     ...                        \\n\",\n       \"8                            0.0                     ...                        \\n\",\n       \"9                            NaN                     ...                        \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Microloan_count  CREDIT_TYPE_Microloan_count_norm  \\\\\\n\",\n       \"0                          0.0                               0.0   \\n\",\n       \"1                          0.0                               0.0   \\n\",\n       \"2                          0.0                               0.0   \\n\",\n       \"3                          NaN                               NaN   \\n\",\n       \"4                          0.0                               0.0   \\n\",\n       \"5                          0.0                               0.0   \\n\",\n       \"6                          0.0                               0.0   \\n\",\n       \"7                          0.0                               0.0   \\n\",\n       \"8                          0.0                               0.0   \\n\",\n       \"9                          NaN                               NaN   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Mobile operator loan_count  \\\\\\n\",\n       \"0                                     0.0   \\n\",\n       \"1                                     0.0   \\n\",\n       \"2                                     0.0   \\n\",\n       \"3                                     NaN   \\n\",\n       \"4                                     0.0   \\n\",\n       \"5                                     0.0   \\n\",\n       \"6                                     0.0   \\n\",\n       \"7                                     0.0   \\n\",\n       \"8                                     0.0   \\n\",\n       \"9                                     NaN   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Mobile operator loan_count_norm  CREDIT_TYPE_Mortgage_count  \\\\\\n\",\n       \"0                                          0.0                         0.0   \\n\",\n       \"1                                          0.0                         0.0   \\n\",\n       \"2                                          0.0                         0.0   \\n\",\n       \"3                                          NaN                         NaN   \\n\",\n       \"4                                          0.0                         0.0   \\n\",\n       \"5                                          0.0                         0.0   \\n\",\n       \"6                                          0.0                         0.0   \\n\",\n       \"7                                          0.0                         0.0   \\n\",\n       \"8                                          0.0                         0.0   \\n\",\n       \"9                                          NaN                         NaN   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Mortgage_count_norm  CREDIT_TYPE_Real estate loan_count  \\\\\\n\",\n       \"0                              0.0                                 0.0   \\n\",\n       \"1                              0.0                                 0.0   \\n\",\n       \"2                              0.0                                 0.0   \\n\",\n       \"3                              NaN                                 NaN   \\n\",\n       \"4                              0.0                                 0.0   \\n\",\n       \"5                              0.0                                 0.0   \\n\",\n       \"6                              0.0                                 0.0   \\n\",\n       \"7                              0.0                                 0.0   \\n\",\n       \"8                              0.0                                 0.0   \\n\",\n       \"9                              NaN                                 NaN   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Real estate loan_count_norm  \\\\\\n\",\n       \"0                                      0.0   \\n\",\n       \"1                                      0.0   \\n\",\n       \"2                                      0.0   \\n\",\n       \"3                                      NaN   \\n\",\n       \"4                                      0.0   \\n\",\n       \"5                                      0.0   \\n\",\n       \"6                                      0.0   \\n\",\n       \"7                                      0.0   \\n\",\n       \"8                                      0.0   \\n\",\n       \"9                                      NaN   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Unknown type of loan_count  \\\\\\n\",\n       \"0                                     0.0   \\n\",\n       \"1                                     0.0   \\n\",\n       \"2                                     0.0   \\n\",\n       \"3                                     NaN   \\n\",\n       \"4                                     0.0   \\n\",\n       \"5                                     0.0   \\n\",\n       \"6                                     0.0   \\n\",\n       \"7                                     0.0   \\n\",\n       \"8                                     0.0   \\n\",\n       \"9                                     NaN   \\n\",\n       \"\\n\",\n       \"   CREDIT_TYPE_Unknown type of loan_count_norm  \\n\",\n       \"0                                          0.0  \\n\",\n       \"1                                          0.0  \\n\",\n       \"2                                          0.0  \\n\",\n       \"3                                          NaN  \\n\",\n       \"4                                          0.0  \\n\",\n       \"5                                          0.0  \\n\",\n       \"6                                          0.0  \\n\",\n       \"7                                          0.0  \\n\",\n       \"8                                          0.0  \\n\",\n       \"9                                          NaN  \\n\",\n       \"\\n\",\n       \"[10 rows x 106 columns]\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.iloc[:10, 123:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"类别变量处理函数\\n\",\n    \"为了使代码更加高效，我们同样对上述方法进行封装，用于处理数据中的类别变量。与agg_numeric函数相同，该函数接受一个数据文件(dataframe)和分组变量(grouping variable)，然后计算数据文件中所有类别变量每个类别的总数和归一化后的数值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def count_categorical(df, group_var, df_name):\\n\",\n    \"    \\\"\\\"\\\"计算数据文件中所有类别变量每个类别的总数和归一化后的数值\\n\",\n    \"    \\n\",\n    \"    参数\\n\",\n    \"    --------\\n\",\n    \"    df : dataframe \\n\",\n    \"        数据文件\\n\",\n    \"        \\n\",\n    \"    group_var : string\\n\",\n    \"        用于将数据文件分组的变量\\n\",\n    \"        对于该变量中的每一个值，最终的数据集中都会存在一行\\n\",\n    \"        \\n\",\n    \"    df_name : string\\n\",\n    \"        用于重命名列名的变量\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    返回值\\n\",\n    \"    --------\\n\",\n    \"    categorical : dataframe\\n\",\n    \"        A返回一个包含类别变量每个类别的总数和归一化后的数值的数据集(dataframe)\\n\",\n    \"        \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # 选择分类列，并对其做one-hot处理\\n\",\n    \"    categorical = pd.get_dummies(df.select_dtypes('object'))\\n\",\n    \"    \\n\",\n    \"    # 确保为分类列添加标识\\n\",\n    \"    categorical[group_var] = df[group_var]\\n\",\n    \"    \\n\",\n    \"    # 按照分类变量分类，计算总和sum和均值mean\\n\",\n    \"    categorical = categorical.groupby(group_var).agg(['sum', 'mean'])\\n\",\n    \"    \\n\",\n    \"    column_names = []\\n\",\n    \"    \\n\",\n    \"    # 迭代变量名\\n\",\n    \"    for var in categorical.columns.levels[0]:\\n\",\n    \"        # 迭代统计量名称sum、mean\\n\",\n    \"        for stat in ['count', 'count_norm']:\\n\",\n    \"            column_names.append('%s_%s_%s' % (df_name, var, stat))\\n\",\n    \"    \\n\",\n    \"    categorical.columns = column_names\\n\",\n    \"    \\n\",\n    \"    return categorical\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Active_count</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Active_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Bad debt_count</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Bad debt_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Closed_count</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Closed_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Sold_count</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Sold_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_CURRENCY_currency 1_count</th>\\n\",\n       \"      <th>bureau_CREDIT_CURRENCY_currency 1_count_norm</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Microloan_count</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Microloan_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Mobile operator loan_count</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Mobile operator loan_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Mortgage_count</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Mortgage_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Real estate loan_count</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Real estate loan_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Unknown type of loan_count</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Unknown type of loan_count_norm</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100001</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.428571</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.571429</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100002</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100003</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100004</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100005</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.666667</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.333333</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 46 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"            bureau_CREDIT_ACTIVE_Active_count  \\\\\\n\",\n       \"SK_ID_CURR                                      \\n\",\n       \"100001                                      3   \\n\",\n       \"100002                                      2   \\n\",\n       \"100003                                      1   \\n\",\n       \"100004                                      0   \\n\",\n       \"100005                                      2   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Active_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                           \\n\",\n       \"100001                                    0.428571   \\n\",\n       \"100002                                    0.250000   \\n\",\n       \"100003                                    0.250000   \\n\",\n       \"100004                                    0.000000   \\n\",\n       \"100005                                    0.666667   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Bad debt_count  \\\\\\n\",\n       \"SK_ID_CURR                                        \\n\",\n       \"100001                                        0   \\n\",\n       \"100002                                        0   \\n\",\n       \"100003                                        0   \\n\",\n       \"100004                                        0   \\n\",\n       \"100005                                        0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Bad debt_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                             \\n\",\n       \"100001                                           0.0   \\n\",\n       \"100002                                           0.0   \\n\",\n       \"100003                                           0.0   \\n\",\n       \"100004                                           0.0   \\n\",\n       \"100005                                           0.0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Closed_count  \\\\\\n\",\n       \"SK_ID_CURR                                      \\n\",\n       \"100001                                      4   \\n\",\n       \"100002                                      6   \\n\",\n       \"100003                                      3   \\n\",\n       \"100004                                      2   \\n\",\n       \"100005                                      1   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Closed_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                           \\n\",\n       \"100001                                    0.571429   \\n\",\n       \"100002                                    0.750000   \\n\",\n       \"100003                                    0.750000   \\n\",\n       \"100004                                    1.000000   \\n\",\n       \"100005                                    0.333333   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Sold_count  \\\\\\n\",\n       \"SK_ID_CURR                                    \\n\",\n       \"100001                                    0   \\n\",\n       \"100002                                    0   \\n\",\n       \"100003                                    0   \\n\",\n       \"100004                                    0   \\n\",\n       \"100005                                    0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Sold_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                         \\n\",\n       \"100001                                       0.0   \\n\",\n       \"100002                                       0.0   \\n\",\n       \"100003                                       0.0   \\n\",\n       \"100004                                       0.0   \\n\",\n       \"100005                                       0.0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_CURRENCY_currency 1_count  \\\\\\n\",\n       \"SK_ID_CURR                                            \\n\",\n       \"100001                                            7   \\n\",\n       \"100002                                            8   \\n\",\n       \"100003                                            4   \\n\",\n       \"100004                                            2   \\n\",\n       \"100005                                            3   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_CURRENCY_currency 1_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                 \\n\",\n       \"100001                                               1.0   \\n\",\n       \"100002                                               1.0   \\n\",\n       \"100003                                               1.0   \\n\",\n       \"100004                                               1.0   \\n\",\n       \"100005                                               1.0   \\n\",\n       \"\\n\",\n       \"                                   ...                          \\\\\\n\",\n       \"SK_ID_CURR                         ...                           \\n\",\n       \"100001                             ...                           \\n\",\n       \"100002                             ...                           \\n\",\n       \"100003                             ...                           \\n\",\n       \"100004                             ...                           \\n\",\n       \"100005                             ...                           \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Microloan_count  \\\\\\n\",\n       \"SK_ID_CURR                                       \\n\",\n       \"100001                                       0   \\n\",\n       \"100002                                       0   \\n\",\n       \"100003                                       0   \\n\",\n       \"100004                                       0   \\n\",\n       \"100005                                       0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Microloan_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                            \\n\",\n       \"100001                                          0.0   \\n\",\n       \"100002                                          0.0   \\n\",\n       \"100003                                          0.0   \\n\",\n       \"100004                                          0.0   \\n\",\n       \"100005                                          0.0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Mobile operator loan_count  \\\\\\n\",\n       \"SK_ID_CURR                                                  \\n\",\n       \"100001                                                  0   \\n\",\n       \"100002                                                  0   \\n\",\n       \"100003                                                  0   \\n\",\n       \"100004                                                  0   \\n\",\n       \"100005                                                  0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Mobile operator loan_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                       \\n\",\n       \"100001                                                    0.0    \\n\",\n       \"100002                                                    0.0    \\n\",\n       \"100003                                                    0.0    \\n\",\n       \"100004                                                    0.0    \\n\",\n       \"100005                                                    0.0    \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Mortgage_count  \\\\\\n\",\n       \"SK_ID_CURR                                      \\n\",\n       \"100001                                      0   \\n\",\n       \"100002                                      0   \\n\",\n       \"100003                                      0   \\n\",\n       \"100004                                      0   \\n\",\n       \"100005                                      0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Mortgage_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                           \\n\",\n       \"100001                                         0.0   \\n\",\n       \"100002                                         0.0   \\n\",\n       \"100003                                         0.0   \\n\",\n       \"100004                                         0.0   \\n\",\n       \"100005                                         0.0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Real estate loan_count  \\\\\\n\",\n       \"SK_ID_CURR                                              \\n\",\n       \"100001                                              0   \\n\",\n       \"100002                                              0   \\n\",\n       \"100003                                              0   \\n\",\n       \"100004                                              0   \\n\",\n       \"100005                                              0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Real estate loan_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                   \\n\",\n       \"100001                                                 0.0   \\n\",\n       \"100002                                                 0.0   \\n\",\n       \"100003                                                 0.0   \\n\",\n       \"100004                                                 0.0   \\n\",\n       \"100005                                                 0.0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Unknown type of loan_count  \\\\\\n\",\n       \"SK_ID_CURR                                                  \\n\",\n       \"100001                                                  0   \\n\",\n       \"100002                                                  0   \\n\",\n       \"100003                                                  0   \\n\",\n       \"100004                                                  0   \\n\",\n       \"100005                                                  0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Unknown type of loan_count_norm  \\n\",\n       \"SK_ID_CURR                                                      \\n\",\n       \"100001                                                    0.0   \\n\",\n       \"100002                                                    0.0   \\n\",\n       \"100003                                                    0.0   \\n\",\n       \"100004                                                    0.0   \\n\",\n       \"100005                                                    0.0   \\n\",\n       \"\\n\",\n       \"[5 rows x 46 columns]\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bureau_counts = count_categorical(bureau, group_var = 'SK_ID_CURR', df_name = 'bureau')\\n\",\n    \"bureau_counts.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 操作bureau_balance数据文件\\n\",\n    \"我们现在转向处理bureau balance文件。这个数据文件中包含了每个客户先前向其他金融机构贷款的月度信息。我们首先按SK_ID_BUREAU（先前的‘贷款ID’）对数据文件进行分组，而不是SK_ID_CURR（当前贷款的‘客户ID’）。通过这样的处理，每一条贷款记录都表示为单独的一行。然后我们可以通过SK_ID_CURR进行分组，并计算每个客户的贷款总额。最终处理的结果是生成一个数据集，其中一行表示一个客户及其贷款的相关统计数据\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_BUREAU</th>\\n\",\n       \"      <th>MONTHS_BALANCE</th>\\n\",\n       \"      <th>STATUS</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>5715448</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>5715448</td>\\n\",\n       \"      <td>-1</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>5715448</td>\\n\",\n       \"      <td>-2</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>5715448</td>\\n\",\n       \"      <td>-3</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5715448</td>\\n\",\n       \"      <td>-4</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_BUREAU  MONTHS_BALANCE STATUS\\n\",\n       \"0       5715448               0      C\\n\",\n       \"1       5715448              -1      C\\n\",\n       \"2       5715448              -2      C\\n\",\n       \"3       5715448              -3      C\\n\",\n       \"4       5715448              -4      C\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 读取bureau_balance数据文件\\n\",\n    \"bureau_balance = pd.read_csv('bureau_balance.csv')\\n\",\n    \"bureau_balance.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"首先，我们可以计算每个贷款每种状态的数量。幸运的是，我们已经有了这样一个函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>bureau_balance_STATUS_0_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_0_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_1_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_1_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_2_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_2_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_3_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_3_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_4_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_4_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_5_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_5_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_C_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_C_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_X_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_X_count_norm</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>SK_ID_BUREAU</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5001709</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>86</td>\\n\",\n       \"      <td>0.886598</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>0.113402</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5001710</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.060241</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>48</td>\\n\",\n       \"      <td>0.578313</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>0.361446</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5001711</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5001712</th>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>0.526316</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.473684</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5001713</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>22</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"              bureau_balance_STATUS_0_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                   0   \\n\",\n       \"5001710                                   5   \\n\",\n       \"5001711                                   3   \\n\",\n       \"5001712                                  10   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_0_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                 0.000000   \\n\",\n       \"5001710                                 0.060241   \\n\",\n       \"5001711                                 0.750000   \\n\",\n       \"5001712                                 0.526316   \\n\",\n       \"5001713                                 0.000000   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_1_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                   0   \\n\",\n       \"5001710                                   0   \\n\",\n       \"5001711                                   0   \\n\",\n       \"5001712                                   0   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_1_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                      0.0   \\n\",\n       \"5001710                                      0.0   \\n\",\n       \"5001711                                      0.0   \\n\",\n       \"5001712                                      0.0   \\n\",\n       \"5001713                                      0.0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_2_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                   0   \\n\",\n       \"5001710                                   0   \\n\",\n       \"5001711                                   0   \\n\",\n       \"5001712                                   0   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_2_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                      0.0   \\n\",\n       \"5001710                                      0.0   \\n\",\n       \"5001711                                      0.0   \\n\",\n       \"5001712                                      0.0   \\n\",\n       \"5001713                                      0.0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_3_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                   0   \\n\",\n       \"5001710                                   0   \\n\",\n       \"5001711                                   0   \\n\",\n       \"5001712                                   0   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_3_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                      0.0   \\n\",\n       \"5001710                                      0.0   \\n\",\n       \"5001711                                      0.0   \\n\",\n       \"5001712                                      0.0   \\n\",\n       \"5001713                                      0.0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_4_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                   0   \\n\",\n       \"5001710                                   0   \\n\",\n       \"5001711                                   0   \\n\",\n       \"5001712                                   0   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_4_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                      0.0   \\n\",\n       \"5001710                                      0.0   \\n\",\n       \"5001711                                      0.0   \\n\",\n       \"5001712                                      0.0   \\n\",\n       \"5001713                                      0.0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_5_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                   0   \\n\",\n       \"5001710                                   0   \\n\",\n       \"5001711                                   0   \\n\",\n       \"5001712                                   0   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_5_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                      0.0   \\n\",\n       \"5001710                                      0.0   \\n\",\n       \"5001711                                      0.0   \\n\",\n       \"5001712                                      0.0   \\n\",\n       \"5001713                                      0.0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_C_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                  86   \\n\",\n       \"5001710                                  48   \\n\",\n       \"5001711                                   0   \\n\",\n       \"5001712                                   9   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_C_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                 0.886598   \\n\",\n       \"5001710                                 0.578313   \\n\",\n       \"5001711                                 0.000000   \\n\",\n       \"5001712                                 0.473684   \\n\",\n       \"5001713                                 0.000000   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_X_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                  11   \\n\",\n       \"5001710                                  30   \\n\",\n       \"5001711                                   1   \\n\",\n       \"5001712                                   0   \\n\",\n       \"5001713                                  22   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_X_count_norm  \\n\",\n       \"SK_ID_BUREAU                                      \\n\",\n       \"5001709                                 0.113402  \\n\",\n       \"5001710                                 0.361446  \\n\",\n       \"5001711                                 0.250000  \\n\",\n       \"5001712                                 0.000000  \\n\",\n       \"5001713                                 1.000000  \"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 计算每个贷款每种状态的数量\\n\",\n    \"bureau_balance_counts = count_categorical(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\\n\",\n    \"bureau_balance_counts.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在我们可以处理一个数值列。MONTHS_BALANCE列包含着相对于申请日期的'月度余额'，这并不一定是一个重要的数值变量，之后我们会考虑将它看做时间变量，但是现在，我们只能按照数值列对它进行与之前相同的聚合统计\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_BUREAU</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_count</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_mean</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_max</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_min</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_sum</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>5001709</td>\\n\",\n       \"      <td>97</td>\\n\",\n       \"      <td>-48.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-96</td>\\n\",\n       \"      <td>-4656</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>5001710</td>\\n\",\n       \"      <td>83</td>\\n\",\n       \"      <td>-41.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-82</td>\\n\",\n       \"      <td>-3403</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>5001711</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-1.5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-3</td>\\n\",\n       \"      <td>-6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>5001712</td>\\n\",\n       \"      <td>19</td>\\n\",\n       \"      <td>-9.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-18</td>\\n\",\n       \"      <td>-171</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5001713</td>\\n\",\n       \"      <td>22</td>\\n\",\n       \"      <td>-10.5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-21</td>\\n\",\n       \"      <td>-231</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_BUREAU  bureau_balance_MONTHS_BALANCE_count  \\\\\\n\",\n       \"0       5001709                                   97   \\n\",\n       \"1       5001710                                   83   \\n\",\n       \"2       5001711                                    4   \\n\",\n       \"3       5001712                                   19   \\n\",\n       \"4       5001713                                   22   \\n\",\n       \"\\n\",\n       \"   bureau_balance_MONTHS_BALANCE_mean  bureau_balance_MONTHS_BALANCE_max  \\\\\\n\",\n       \"0                               -48.0                                  0   \\n\",\n       \"1                               -41.0                                  0   \\n\",\n       \"2                                -1.5                                  0   \\n\",\n       \"3                                -9.0                                  0   \\n\",\n       \"4                               -10.5                                  0   \\n\",\n       \"\\n\",\n       \"   bureau_balance_MONTHS_BALANCE_min  bureau_balance_MONTHS_BALANCE_sum  \\n\",\n       \"0                                -96                              -4656  \\n\",\n       \"1                                -82                              -3403  \\n\",\n       \"2                                 -3                                 -6  \\n\",\n       \"3                                -18                               -171  \\n\",\n       \"4                                -21                               -231  \"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 根据'SK_ID_CURR’进行数值统计\\n\",\n    \"bureau_balance_agg = agg_numeric(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\\n\",\n    \"bureau_balance_agg.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"上面的数据对每个贷款都进行了计算。现在需要将这些数据和相应的客户结合在一起。首先将数据文件合并在一起，因为所有变量都是数值的，所以只需按SK_ID_CURR分组，再次聚合统计数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_BUREAU</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_count</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_mean</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_max</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_min</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_sum</th>\\n\",\n       \"      <th>bureau_balance_STATUS_0_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_0_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_1_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_1_count_norm</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>bureau_balance_STATUS_3_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_4_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_4_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_5_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_5_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_C_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_C_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_X_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_X_count_norm</th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>5001709</td>\\n\",\n       \"      <td>97</td>\\n\",\n       \"      <td>-48.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-96</td>\\n\",\n       \"      <td>-4656</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>86</td>\\n\",\n       \"      <td>0.886598</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>0.113402</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>5001710</td>\\n\",\n       \"      <td>83</td>\\n\",\n       \"      <td>-41.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-82</td>\\n\",\n       \"      <td>-3403</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.060241</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>48</td>\\n\",\n       \"      <td>0.578313</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>0.361446</td>\\n\",\n       \"      <td>162368.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>5001711</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-1.5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-3</td>\\n\",\n       \"      <td>-6</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"      <td>162368.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>5001712</td>\\n\",\n       \"      <td>19</td>\\n\",\n       \"      <td>-9.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-18</td>\\n\",\n       \"      <td>-171</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>0.526316</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.473684</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>162368.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5001713</td>\\n\",\n       \"      <td>22</td>\\n\",\n       \"      <td>-10.5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-21</td>\\n\",\n       \"      <td>-231</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>22</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>150635.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 23 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_BUREAU  bureau_balance_MONTHS_BALANCE_count  \\\\\\n\",\n       \"0       5001709                                   97   \\n\",\n       \"1       5001710                                   83   \\n\",\n       \"2       5001711                                    4   \\n\",\n       \"3       5001712                                   19   \\n\",\n       \"4       5001713                                   22   \\n\",\n       \"\\n\",\n       \"   bureau_balance_MONTHS_BALANCE_mean  bureau_balance_MONTHS_BALANCE_max  \\\\\\n\",\n       \"0                               -48.0                                  0   \\n\",\n       \"1                               -41.0                                  0   \\n\",\n       \"2                                -1.5                                  0   \\n\",\n       \"3                                -9.0                                  0   \\n\",\n       \"4                               -10.5                                  0   \\n\",\n       \"\\n\",\n       \"   bureau_balance_MONTHS_BALANCE_min  bureau_balance_MONTHS_BALANCE_sum  \\\\\\n\",\n       \"0                                -96                              -4656   \\n\",\n       \"1                                -82                              -3403   \\n\",\n       \"2                                 -3                                 -6   \\n\",\n       \"3                                -18                               -171   \\n\",\n       \"4                                -21                               -231   \\n\",\n       \"\\n\",\n       \"   bureau_balance_STATUS_0_count  bureau_balance_STATUS_0_count_norm  \\\\\\n\",\n       \"0                              0                            0.000000   \\n\",\n       \"1                              5                            0.060241   \\n\",\n       \"2                              3                            0.750000   \\n\",\n       \"3                             10                            0.526316   \\n\",\n       \"4                              0                            0.000000   \\n\",\n       \"\\n\",\n       \"   bureau_balance_STATUS_1_count  bureau_balance_STATUS_1_count_norm  \\\\\\n\",\n       \"0                              0                                 0.0   \\n\",\n       \"1                              0                                 0.0   \\n\",\n       \"2                              0                                 0.0   \\n\",\n       \"3                              0                                 0.0   \\n\",\n       \"4                              0                                 0.0   \\n\",\n       \"\\n\",\n       \"      ...      bureau_balance_STATUS_3_count_norm  \\\\\\n\",\n       \"0     ...                                     0.0   \\n\",\n       \"1     ...                                     0.0   \\n\",\n       \"2     ...                                     0.0   \\n\",\n       \"3     ...                                     0.0   \\n\",\n       \"4     ...                                     0.0   \\n\",\n       \"\\n\",\n       \"   bureau_balance_STATUS_4_count  bureau_balance_STATUS_4_count_norm  \\\\\\n\",\n       \"0                              0                                 0.0   \\n\",\n       \"1                              0                                 0.0   \\n\",\n       \"2                              0                                 0.0   \\n\",\n       \"3                              0                                 0.0   \\n\",\n       \"4                              0                                 0.0   \\n\",\n       \"\\n\",\n       \"   bureau_balance_STATUS_5_count  bureau_balance_STATUS_5_count_norm  \\\\\\n\",\n       \"0                              0                                 0.0   \\n\",\n       \"1                              0                                 0.0   \\n\",\n       \"2                              0                                 0.0   \\n\",\n       \"3                              0                                 0.0   \\n\",\n       \"4                              0                                 0.0   \\n\",\n       \"\\n\",\n       \"   bureau_balance_STATUS_C_count  bureau_balance_STATUS_C_count_norm  \\\\\\n\",\n       \"0                             86                            0.886598   \\n\",\n       \"1                             48                            0.578313   \\n\",\n       \"2                              0                            0.000000   \\n\",\n       \"3                              9                            0.473684   \\n\",\n       \"4                              0                            0.000000   \\n\",\n       \"\\n\",\n       \"   bureau_balance_STATUS_X_count  bureau_balance_STATUS_X_count_norm  \\\\\\n\",\n       \"0                             11                            0.113402   \\n\",\n       \"1                             30                            0.361446   \\n\",\n       \"2                              1                            0.250000   \\n\",\n       \"3                              0                            0.000000   \\n\",\n       \"4                             22                            1.000000   \\n\",\n       \"\\n\",\n       \"   SK_ID_CURR  \\n\",\n       \"0         NaN  \\n\",\n       \"1    162368.0  \\n\",\n       \"2    162368.0  \\n\",\n       \"3    162368.0  \\n\",\n       \"4    150635.0  \\n\",\n       \"\\n\",\n       \"[5 rows x 23 columns]\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 按照贷款将数据文件分组\\n\",\n    \"bureau_by_loan = bureau_balance_agg.merge(bureau_balance_counts, right_index = True, left_on = 'SK_ID_BUREAU', how = 'outer')\\n\",\n    \"\\n\",\n    \"# 合并（包括SK_ID_CURR）\\n\",\n    \"bureau_by_loan = bureau_by_loan.merge(bureau[['SK_ID_BUREAU', 'SK_ID_CURR']], on = 'SK_ID_BUREAU', how = 'left')\\n\",\n    \"# how = left 只保留左表的所有数据，如果右表的SK_ID_BUREAU列中出现了左表中没有的值，那么该值所对应的行数据会被舍弃(right同理)\\n\",\n    \"bureau_by_loan.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_count_count</th>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_count_mean</th>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_count_max</th>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_count_min</th>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_count_sum</th>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_mean_count</th>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_mean_mean</th>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_mean_max</th>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_mean_min</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>client_bureau_balance_STATUS_X_count_count</th>\\n\",\n       \"      <th>client_bureau_balance_STATUS_X_count_mean</th>\\n\",\n       \"      <th>client_bureau_balance_STATUS_X_count_max</th>\\n\",\n       \"      <th>client_bureau_balance_STATUS_X_count_min</th>\\n\",\n       \"      <th>client_bureau_balance_STATUS_X_count_sum</th>\\n\",\n       \"      <th>client_bureau_balance_STATUS_X_count_norm_count</th>\\n\",\n       \"      <th>client_bureau_balance_STATUS_X_count_norm_mean</th>\\n\",\n       \"      <th>client_bureau_balance_STATUS_X_count_norm_max</th>\\n\",\n       \"      <th>client_bureau_balance_STATUS_X_count_norm_min</th>\\n\",\n       \"      <th>client_bureau_balance_STATUS_X_count_norm_sum</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>100001.0</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>24.571429</td>\\n\",\n       \"      <td>52</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>172</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-11.785714</td>\\n\",\n       \"      <td>-0.5</td>\\n\",\n       \"      <td>-25.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>4.285714</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>30.0</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.214590</td>\\n\",\n       \"      <td>0.500000</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.502129</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>100002.0</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>13.750000</td>\\n\",\n       \"      <td>22</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>110</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-21.875000</td>\\n\",\n       \"      <td>-1.5</td>\\n\",\n       \"      <td>-39.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>1.875000</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>15.0</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.161932</td>\\n\",\n       \"      <td>0.500000</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.295455</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>100005.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>7.000000</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>21</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>-3.000000</td>\\n\",\n       \"      <td>-1.0</td>\\n\",\n       \"      <td>-6.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.666667</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.136752</td>\\n\",\n       \"      <td>0.333333</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.410256</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>100010.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>36.000000</td>\\n\",\n       \"      <td>36</td>\\n\",\n       \"      <td>36</td>\\n\",\n       \"      <td>72</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>-46.000000</td>\\n\",\n       \"      <td>-19.5</td>\\n\",\n       \"      <td>-72.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>100013.0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>57.500000</td>\\n\",\n       \"      <td>69</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>230</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-28.250000</td>\\n\",\n       \"      <td>-19.5</td>\\n\",\n       \"      <td>-34.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>10.250000</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>41.0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.254545</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.018182</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 106 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_CURR  client_bureau_balance_MONTHS_BALANCE_count_count  \\\\\\n\",\n       \"0    100001.0                                                 7   \\n\",\n       \"1    100002.0                                                 8   \\n\",\n       \"2    100005.0                                                 3   \\n\",\n       \"3    100010.0                                                 2   \\n\",\n       \"4    100013.0                                                 4   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_MONTHS_BALANCE_count_mean  \\\\\\n\",\n       \"0                                        24.571429   \\n\",\n       \"1                                        13.750000   \\n\",\n       \"2                                         7.000000   \\n\",\n       \"3                                        36.000000   \\n\",\n       \"4                                        57.500000   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_MONTHS_BALANCE_count_max  \\\\\\n\",\n       \"0                                              52   \\n\",\n       \"1                                              22   \\n\",\n       \"2                                              13   \\n\",\n       \"3                                              36   \\n\",\n       \"4                                              69   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_MONTHS_BALANCE_count_min  \\\\\\n\",\n       \"0                                               2   \\n\",\n       \"1                                               4   \\n\",\n       \"2                                               3   \\n\",\n       \"3                                              36   \\n\",\n       \"4                                              40   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_MONTHS_BALANCE_count_sum  \\\\\\n\",\n       \"0                                             172   \\n\",\n       \"1                                             110   \\n\",\n       \"2                                              21   \\n\",\n       \"3                                              72   \\n\",\n       \"4                                             230   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_MONTHS_BALANCE_mean_count  \\\\\\n\",\n       \"0                                                7   \\n\",\n       \"1                                                8   \\n\",\n       \"2                                                3   \\n\",\n       \"3                                                2   \\n\",\n       \"4                                                4   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_MONTHS_BALANCE_mean_mean  \\\\\\n\",\n       \"0                                      -11.785714   \\n\",\n       \"1                                      -21.875000   \\n\",\n       \"2                                       -3.000000   \\n\",\n       \"3                                      -46.000000   \\n\",\n       \"4                                      -28.250000   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_MONTHS_BALANCE_mean_max  \\\\\\n\",\n       \"0                                           -0.5   \\n\",\n       \"1                                           -1.5   \\n\",\n       \"2                                           -1.0   \\n\",\n       \"3                                          -19.5   \\n\",\n       \"4                                          -19.5   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_MONTHS_BALANCE_mean_min  \\\\\\n\",\n       \"0                                          -25.5   \\n\",\n       \"1                                          -39.5   \\n\",\n       \"2                                           -6.0   \\n\",\n       \"3                                          -72.5   \\n\",\n       \"4                                          -34.0   \\n\",\n       \"\\n\",\n       \"                       ...                        \\\\\\n\",\n       \"0                      ...                         \\n\",\n       \"1                      ...                         \\n\",\n       \"2                      ...                         \\n\",\n       \"3                      ...                         \\n\",\n       \"4                      ...                         \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_STATUS_X_count_count  \\\\\\n\",\n       \"0                                           7   \\n\",\n       \"1                                           8   \\n\",\n       \"2                                           3   \\n\",\n       \"3                                           2   \\n\",\n       \"4                                           4   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_STATUS_X_count_mean  \\\\\\n\",\n       \"0                                   4.285714   \\n\",\n       \"1                                   1.875000   \\n\",\n       \"2                                   0.666667   \\n\",\n       \"3                                   0.000000   \\n\",\n       \"4                                  10.250000   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_STATUS_X_count_max  \\\\\\n\",\n       \"0                                         9   \\n\",\n       \"1                                         3   \\n\",\n       \"2                                         1   \\n\",\n       \"3                                         0   \\n\",\n       \"4                                        40   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_STATUS_X_count_min  \\\\\\n\",\n       \"0                                         0   \\n\",\n       \"1                                         0   \\n\",\n       \"2                                         0   \\n\",\n       \"3                                         0   \\n\",\n       \"4                                         0   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_STATUS_X_count_sum  \\\\\\n\",\n       \"0                                      30.0   \\n\",\n       \"1                                      15.0   \\n\",\n       \"2                                       2.0   \\n\",\n       \"3                                       0.0   \\n\",\n       \"4                                      41.0   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_STATUS_X_count_norm_count  \\\\\\n\",\n       \"0                                                7   \\n\",\n       \"1                                                8   \\n\",\n       \"2                                                3   \\n\",\n       \"3                                                2   \\n\",\n       \"4                                                4   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_STATUS_X_count_norm_mean  \\\\\\n\",\n       \"0                                        0.214590   \\n\",\n       \"1                                        0.161932   \\n\",\n       \"2                                        0.136752   \\n\",\n       \"3                                        0.000000   \\n\",\n       \"4                                        0.254545   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_STATUS_X_count_norm_max  \\\\\\n\",\n       \"0                                       0.500000   \\n\",\n       \"1                                       0.500000   \\n\",\n       \"2                                       0.333333   \\n\",\n       \"3                                       0.000000   \\n\",\n       \"4                                       1.000000   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_STATUS_X_count_norm_min  \\\\\\n\",\n       \"0                                            0.0   \\n\",\n       \"1                                            0.0   \\n\",\n       \"2                                            0.0   \\n\",\n       \"3                                            0.0   \\n\",\n       \"4                                            0.0   \\n\",\n       \"\\n\",\n       \"   client_bureau_balance_STATUS_X_count_norm_sum  \\n\",\n       \"0                                       1.502129  \\n\",\n       \"1                                       1.295455  \\n\",\n       \"2                                       0.410256  \\n\",\n       \"3                                       0.000000  \\n\",\n       \"4                                       1.018182  \\n\",\n       \"\\n\",\n       \"[5 rows x 106 columns]\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bureau_balance_by_client = agg_numeric(bureau_by_loan.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'client')\\n\",\n    \"bureau_balance_by_client.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"总而言之，对于bureau_balance数据文件，我们做了以下处理：\\n\",\n    \"\\n\",\n    \"1、计算分组后贷款数据的相关统计量(各个status的贷款数量)\\n\",\n    \"2、对按贷款分类后的每个类别变量(categorical variable)进行相关统计量计算\\n\",\n    \"3、合并训练数据和计算得到的贷款统计量\\n\",\n    \"4、对数据集按客户id进行分组，再计算数值型数据\\n\",\n    \"\\n\",\n    \"最终得到的数据，每一行代表一个客户，其中的统计信息是针对该客户所有贷款计算的，并带有月度余额信息\\n\",\n    \"\\n\",\n    \"一些变量有点混乱，所以需要解释一下：\\n\",\n    \"client_bureau_balance_MONTHS_BALANCE_mean_mean：计算MONTHS_BALANCE中每一笔贷款的平均值。然后为每个客户计算其所有贷款中该数值的平均值\\n\",\n    \"client_bureau_balance_STATUS_X_count_norm_sum：对每个贷款统计STATUS==X的发生次数，并除以贷款的总STATUS值。然后，为每个客户计算其所有贷款中该数值的总和\\n\",\n    \"\\n\",\n    \"在我们将所有变量组合到一个数据集之前，先不计算变量的相关系数\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 功能集成\\n\",\n    \"我们已经准备好从其他金融机构的历史贷款数据和这些贷款的月偿还数据中获取信息，并将这些信息与训练数据集结合。我们重新设置所有变量，然后使用我们所构建的函数来完成该工作，这个过程展示了复用的好处\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"112\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 删除旧对象释放内存\\n\",\n    \"import gc\\n\",\n    \"gc.enable()\\n\",\n    \"del train, bureau, bureau_balance, bureau_agg, bureau_agg_new, bureau_balance_agg, bureau_balance_counts, bureau_by_loan, bureau_balance_by_client, bureau_counts\\n\",\n    \"gc.collect()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 从所有数据集中读取新的副本\\n\",\n    \"train = pd.read_csv('application_train.csv')\\n\",\n    \"bureau = pd.read_csv('bureau.csv')\\n\",\n    \"bureau_balance = pd.read_csv('bureau_balance.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Bureau数据集计数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Active_count</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Active_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Bad debt_count</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Bad debt_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Closed_count</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Closed_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Sold_count</th>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Sold_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_CURRENCY_currency 1_count</th>\\n\",\n       \"      <th>bureau_CREDIT_CURRENCY_currency 1_count_norm</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Microloan_count</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Microloan_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Mobile operator loan_count</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Mobile operator loan_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Mortgage_count</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Mortgage_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Real estate loan_count</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Real estate loan_count_norm</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Unknown type of loan_count</th>\\n\",\n       \"      <th>bureau_CREDIT_TYPE_Unknown type of loan_count_norm</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100001</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.428571</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.571429</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100002</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100003</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100004</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100005</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.666667</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.333333</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 46 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"            bureau_CREDIT_ACTIVE_Active_count  \\\\\\n\",\n       \"SK_ID_CURR                                      \\n\",\n       \"100001                                      3   \\n\",\n       \"100002                                      2   \\n\",\n       \"100003                                      1   \\n\",\n       \"100004                                      0   \\n\",\n       \"100005                                      2   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Active_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                           \\n\",\n       \"100001                                    0.428571   \\n\",\n       \"100002                                    0.250000   \\n\",\n       \"100003                                    0.250000   \\n\",\n       \"100004                                    0.000000   \\n\",\n       \"100005                                    0.666667   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Bad debt_count  \\\\\\n\",\n       \"SK_ID_CURR                                        \\n\",\n       \"100001                                        0   \\n\",\n       \"100002                                        0   \\n\",\n       \"100003                                        0   \\n\",\n       \"100004                                        0   \\n\",\n       \"100005                                        0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Bad debt_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                             \\n\",\n       \"100001                                           0.0   \\n\",\n       \"100002                                           0.0   \\n\",\n       \"100003                                           0.0   \\n\",\n       \"100004                                           0.0   \\n\",\n       \"100005                                           0.0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Closed_count  \\\\\\n\",\n       \"SK_ID_CURR                                      \\n\",\n       \"100001                                      4   \\n\",\n       \"100002                                      6   \\n\",\n       \"100003                                      3   \\n\",\n       \"100004                                      2   \\n\",\n       \"100005                                      1   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Closed_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                           \\n\",\n       \"100001                                    0.571429   \\n\",\n       \"100002                                    0.750000   \\n\",\n       \"100003                                    0.750000   \\n\",\n       \"100004                                    1.000000   \\n\",\n       \"100005                                    0.333333   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Sold_count  \\\\\\n\",\n       \"SK_ID_CURR                                    \\n\",\n       \"100001                                    0   \\n\",\n       \"100002                                    0   \\n\",\n       \"100003                                    0   \\n\",\n       \"100004                                    0   \\n\",\n       \"100005                                    0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_ACTIVE_Sold_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                         \\n\",\n       \"100001                                       0.0   \\n\",\n       \"100002                                       0.0   \\n\",\n       \"100003                                       0.0   \\n\",\n       \"100004                                       0.0   \\n\",\n       \"100005                                       0.0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_CURRENCY_currency 1_count  \\\\\\n\",\n       \"SK_ID_CURR                                            \\n\",\n       \"100001                                            7   \\n\",\n       \"100002                                            8   \\n\",\n       \"100003                                            4   \\n\",\n       \"100004                                            2   \\n\",\n       \"100005                                            3   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_CURRENCY_currency 1_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                 \\n\",\n       \"100001                                               1.0   \\n\",\n       \"100002                                               1.0   \\n\",\n       \"100003                                               1.0   \\n\",\n       \"100004                                               1.0   \\n\",\n       \"100005                                               1.0   \\n\",\n       \"\\n\",\n       \"                                   ...                          \\\\\\n\",\n       \"SK_ID_CURR                         ...                           \\n\",\n       \"100001                             ...                           \\n\",\n       \"100002                             ...                           \\n\",\n       \"100003                             ...                           \\n\",\n       \"100004                             ...                           \\n\",\n       \"100005                             ...                           \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Microloan_count  \\\\\\n\",\n       \"SK_ID_CURR                                       \\n\",\n       \"100001                                       0   \\n\",\n       \"100002                                       0   \\n\",\n       \"100003                                       0   \\n\",\n       \"100004                                       0   \\n\",\n       \"100005                                       0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Microloan_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                            \\n\",\n       \"100001                                          0.0   \\n\",\n       \"100002                                          0.0   \\n\",\n       \"100003                                          0.0   \\n\",\n       \"100004                                          0.0   \\n\",\n       \"100005                                          0.0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Mobile operator loan_count  \\\\\\n\",\n       \"SK_ID_CURR                                                  \\n\",\n       \"100001                                                  0   \\n\",\n       \"100002                                                  0   \\n\",\n       \"100003                                                  0   \\n\",\n       \"100004                                                  0   \\n\",\n       \"100005                                                  0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Mobile operator loan_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                       \\n\",\n       \"100001                                                    0.0    \\n\",\n       \"100002                                                    0.0    \\n\",\n       \"100003                                                    0.0    \\n\",\n       \"100004                                                    0.0    \\n\",\n       \"100005                                                    0.0    \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Mortgage_count  \\\\\\n\",\n       \"SK_ID_CURR                                      \\n\",\n       \"100001                                      0   \\n\",\n       \"100002                                      0   \\n\",\n       \"100003                                      0   \\n\",\n       \"100004                                      0   \\n\",\n       \"100005                                      0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Mortgage_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                           \\n\",\n       \"100001                                         0.0   \\n\",\n       \"100002                                         0.0   \\n\",\n       \"100003                                         0.0   \\n\",\n       \"100004                                         0.0   \\n\",\n       \"100005                                         0.0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Real estate loan_count  \\\\\\n\",\n       \"SK_ID_CURR                                              \\n\",\n       \"100001                                              0   \\n\",\n       \"100002                                              0   \\n\",\n       \"100003                                              0   \\n\",\n       \"100004                                              0   \\n\",\n       \"100005                                              0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Real estate loan_count_norm  \\\\\\n\",\n       \"SK_ID_CURR                                                   \\n\",\n       \"100001                                                 0.0   \\n\",\n       \"100002                                                 0.0   \\n\",\n       \"100003                                                 0.0   \\n\",\n       \"100004                                                 0.0   \\n\",\n       \"100005                                                 0.0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Unknown type of loan_count  \\\\\\n\",\n       \"SK_ID_CURR                                                  \\n\",\n       \"100001                                                  0   \\n\",\n       \"100002                                                  0   \\n\",\n       \"100003                                                  0   \\n\",\n       \"100004                                                  0   \\n\",\n       \"100005                                                  0   \\n\",\n       \"\\n\",\n       \"            bureau_CREDIT_TYPE_Unknown type of loan_count_norm  \\n\",\n       \"SK_ID_CURR                                                      \\n\",\n       \"100001                                                    0.0   \\n\",\n       \"100002                                                    0.0   \\n\",\n       \"100003                                                    0.0   \\n\",\n       \"100004                                                    0.0   \\n\",\n       \"100005                                                    0.0   \\n\",\n       \"\\n\",\n       \"[5 rows x 46 columns]\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bureau_counts = count_categorical(bureau, group_var = 'SK_ID_CURR', df_name = 'bureau')\\n\",\n    \"bureau_counts.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Bureau数据集聚合（计算mean、max、min、sum等统计量）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_count</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_mean</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_max</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_min</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_sum</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_count</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_mean</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_max</th>\\n\",\n       \"      <th>bureau_CREDIT_DAY_OVERDUE_min</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_count</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_min</th>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_sum</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_count</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_mean</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_max</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_min</th>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_sum</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>100001</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-735.000000</td>\\n\",\n       \"      <td>-49</td>\\n\",\n       \"      <td>-1572</td>\\n\",\n       \"      <td>-5145</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-93.142857</td>\\n\",\n       \"      <td>-6</td>\\n\",\n       \"      <td>-155</td>\\n\",\n       \"      <td>-652</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>3545.357143</td>\\n\",\n       \"      <td>10822.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>24817.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>100002</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-874.000000</td>\\n\",\n       \"      <td>-103</td>\\n\",\n       \"      <td>-1437</td>\\n\",\n       \"      <td>-6992</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>-499.875000</td>\\n\",\n       \"      <td>-7</td>\\n\",\n       \"      <td>-1185</td>\\n\",\n       \"      <td>-3999</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>100003</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-1400.750000</td>\\n\",\n       \"      <td>-606</td>\\n\",\n       \"      <td>-2586</td>\\n\",\n       \"      <td>-5603</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-816.000000</td>\\n\",\n       \"      <td>-43</td>\\n\",\n       \"      <td>-2131</td>\\n\",\n       \"      <td>-3264</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>100004</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>-867.000000</td>\\n\",\n       \"      <td>-408</td>\\n\",\n       \"      <td>-1326</td>\\n\",\n       \"      <td>-1734</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>-532.000000</td>\\n\",\n       \"      <td>-382</td>\\n\",\n       \"      <td>-682</td>\\n\",\n       \"      <td>-1064</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>100005</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>-190.666667</td>\\n\",\n       \"      <td>-62</td>\\n\",\n       \"      <td>-373</td>\\n\",\n       \"      <td>-572</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>-54.333333</td>\\n\",\n       \"      <td>-11</td>\\n\",\n       \"      <td>-121</td>\\n\",\n       \"      <td>-163</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1420.500000</td>\\n\",\n       \"      <td>4261.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4261.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 61 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_CURR  bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  \\\\\\n\",\n       \"0      100001                         7              -735.000000   \\n\",\n       \"1      100002                         8              -874.000000   \\n\",\n       \"2      100003                         4             -1400.750000   \\n\",\n       \"3      100004                         2              -867.000000   \\n\",\n       \"4      100005                         3              -190.666667   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_max  bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\\\\n\",\n       \"0                     -49                   -1572                   -5145   \\n\",\n       \"1                    -103                   -1437                   -6992   \\n\",\n       \"2                    -606                   -2586                   -5603   \\n\",\n       \"3                    -408                   -1326                   -1734   \\n\",\n       \"4                     -62                    -373                    -572   \\n\",\n       \"\\n\",\n       \"   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\\\\n\",\n       \"0                                7                             0.0   \\n\",\n       \"1                                8                             0.0   \\n\",\n       \"2                                4                             0.0   \\n\",\n       \"3                                2                             0.0   \\n\",\n       \"4                                3                             0.0   \\n\",\n       \"\\n\",\n       \"   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  \\\\\\n\",\n       \"0                              0                              0   \\n\",\n       \"1                              0                              0   \\n\",\n       \"2                              0                              0   \\n\",\n       \"3                              0                              0   \\n\",\n       \"4                              0                              0   \\n\",\n       \"\\n\",\n       \"            ...            bureau_DAYS_CREDIT_UPDATE_count  \\\\\\n\",\n       \"0           ...                                          7   \\n\",\n       \"1           ...                                          8   \\n\",\n       \"2           ...                                          4   \\n\",\n       \"3           ...                                          2   \\n\",\n       \"4           ...                                          3   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_UPDATE_mean  bureau_DAYS_CREDIT_UPDATE_max  \\\\\\n\",\n       \"0                      -93.142857                             -6   \\n\",\n       \"1                     -499.875000                             -7   \\n\",\n       \"2                     -816.000000                            -43   \\n\",\n       \"3                     -532.000000                           -382   \\n\",\n       \"4                      -54.333333                            -11   \\n\",\n       \"\\n\",\n       \"   bureau_DAYS_CREDIT_UPDATE_min  bureau_DAYS_CREDIT_UPDATE_sum  \\\\\\n\",\n       \"0                           -155                           -652   \\n\",\n       \"1                          -1185                          -3999   \\n\",\n       \"2                          -2131                          -3264   \\n\",\n       \"3                           -682                          -1064   \\n\",\n       \"4                           -121                           -163   \\n\",\n       \"\\n\",\n       \"   bureau_AMT_ANNUITY_count  bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  \\\\\\n\",\n       \"0                         7              3545.357143                 10822.5   \\n\",\n       \"1                         7                 0.000000                     0.0   \\n\",\n       \"2                         0                      NaN                     NaN   \\n\",\n       \"3                         0                      NaN                     NaN   \\n\",\n       \"4                         3              1420.500000                  4261.5   \\n\",\n       \"\\n\",\n       \"   bureau_AMT_ANNUITY_min  bureau_AMT_ANNUITY_sum  \\n\",\n       \"0                     0.0                 24817.5  \\n\",\n       \"1                     0.0                     0.0  \\n\",\n       \"2                     NaN                     0.0  \\n\",\n       \"3                     NaN                     0.0  \\n\",\n       \"4                     0.0                  4261.5  \\n\",\n       \"\\n\",\n       \"[5 rows x 61 columns]\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bureau_agg = agg_numeric(bureau.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'bureau')\\n\",\n    \"bureau_agg.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 按贷款对Bureau Balance数据集计数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>bureau_balance_STATUS_0_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_0_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_1_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_1_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_2_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_2_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_3_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_3_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_4_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_4_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_5_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_5_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_C_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_C_count_norm</th>\\n\",\n       \"      <th>bureau_balance_STATUS_X_count</th>\\n\",\n       \"      <th>bureau_balance_STATUS_X_count_norm</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>SK_ID_BUREAU</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5001709</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>86</td>\\n\",\n       \"      <td>0.886598</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>0.113402</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5001710</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.060241</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>48</td>\\n\",\n       \"      <td>0.578313</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>0.361446</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5001711</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5001712</th>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>0.526316</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.473684</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5001713</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>22</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"              bureau_balance_STATUS_0_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                   0   \\n\",\n       \"5001710                                   5   \\n\",\n       \"5001711                                   3   \\n\",\n       \"5001712                                  10   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_0_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                 0.000000   \\n\",\n       \"5001710                                 0.060241   \\n\",\n       \"5001711                                 0.750000   \\n\",\n       \"5001712                                 0.526316   \\n\",\n       \"5001713                                 0.000000   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_1_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                   0   \\n\",\n       \"5001710                                   0   \\n\",\n       \"5001711                                   0   \\n\",\n       \"5001712                                   0   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_1_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                      0.0   \\n\",\n       \"5001710                                      0.0   \\n\",\n       \"5001711                                      0.0   \\n\",\n       \"5001712                                      0.0   \\n\",\n       \"5001713                                      0.0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_2_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                   0   \\n\",\n       \"5001710                                   0   \\n\",\n       \"5001711                                   0   \\n\",\n       \"5001712                                   0   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_2_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                      0.0   \\n\",\n       \"5001710                                      0.0   \\n\",\n       \"5001711                                      0.0   \\n\",\n       \"5001712                                      0.0   \\n\",\n       \"5001713                                      0.0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_3_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                   0   \\n\",\n       \"5001710                                   0   \\n\",\n       \"5001711                                   0   \\n\",\n       \"5001712                                   0   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_3_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                      0.0   \\n\",\n       \"5001710                                      0.0   \\n\",\n       \"5001711                                      0.0   \\n\",\n       \"5001712                                      0.0   \\n\",\n       \"5001713                                      0.0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_4_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                   0   \\n\",\n       \"5001710                                   0   \\n\",\n       \"5001711                                   0   \\n\",\n       \"5001712                                   0   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_4_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                      0.0   \\n\",\n       \"5001710                                      0.0   \\n\",\n       \"5001711                                      0.0   \\n\",\n       \"5001712                                      0.0   \\n\",\n       \"5001713                                      0.0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_5_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                   0   \\n\",\n       \"5001710                                   0   \\n\",\n       \"5001711                                   0   \\n\",\n       \"5001712                                   0   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_5_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                      0.0   \\n\",\n       \"5001710                                      0.0   \\n\",\n       \"5001711                                      0.0   \\n\",\n       \"5001712                                      0.0   \\n\",\n       \"5001713                                      0.0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_C_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                  86   \\n\",\n       \"5001710                                  48   \\n\",\n       \"5001711                                   0   \\n\",\n       \"5001712                                   9   \\n\",\n       \"5001713                                   0   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_C_count_norm  \\\\\\n\",\n       \"SK_ID_BUREAU                                       \\n\",\n       \"5001709                                 0.886598   \\n\",\n       \"5001710                                 0.578313   \\n\",\n       \"5001711                                 0.000000   \\n\",\n       \"5001712                                 0.473684   \\n\",\n       \"5001713                                 0.000000   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_X_count  \\\\\\n\",\n       \"SK_ID_BUREAU                                  \\n\",\n       \"5001709                                  11   \\n\",\n       \"5001710                                  30   \\n\",\n       \"5001711                                   1   \\n\",\n       \"5001712                                   0   \\n\",\n       \"5001713                                  22   \\n\",\n       \"\\n\",\n       \"              bureau_balance_STATUS_X_count_norm  \\n\",\n       \"SK_ID_BUREAU                                      \\n\",\n       \"5001709                                 0.113402  \\n\",\n       \"5001710                                 0.361446  \\n\",\n       \"5001711                                 0.250000  \\n\",\n       \"5001712                                 0.000000  \\n\",\n       \"5001713                                 1.000000  \"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bureau_balance_counts = count_categorical(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\\n\",\n    \"bureau_balance_counts.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 按贷款对Bureau Balance数据集聚合\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_BUREAU</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_count</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_mean</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_max</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_min</th>\\n\",\n       \"      <th>bureau_balance_MONTHS_BALANCE_sum</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>5001709</td>\\n\",\n       \"      <td>97</td>\\n\",\n       \"      <td>-48.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-96</td>\\n\",\n       \"      <td>-4656</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>5001710</td>\\n\",\n       \"      <td>83</td>\\n\",\n       \"      <td>-41.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-82</td>\\n\",\n       \"      <td>-3403</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>5001711</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>-1.5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-3</td>\\n\",\n       \"      <td>-6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>5001712</td>\\n\",\n       \"      <td>19</td>\\n\",\n       \"      <td>-9.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-18</td>\\n\",\n       \"      <td>-171</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5001713</td>\\n\",\n       \"      <td>22</td>\\n\",\n       \"      <td>-10.5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>-21</td>\\n\",\n       \"      <td>-231</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_BUREAU  bureau_balance_MONTHS_BALANCE_count  \\\\\\n\",\n       \"0       5001709                                   97   \\n\",\n       \"1       5001710                                   83   \\n\",\n       \"2       5001711                                    4   \\n\",\n       \"3       5001712                                   19   \\n\",\n       \"4       5001713                                   22   \\n\",\n       \"\\n\",\n       \"   bureau_balance_MONTHS_BALANCE_mean  bureau_balance_MONTHS_BALANCE_max  \\\\\\n\",\n       \"0                               -48.0                                  0   \\n\",\n       \"1                               -41.0                                  0   \\n\",\n       \"2                                -1.5                                  0   \\n\",\n       \"3                                -9.0                                  0   \\n\",\n       \"4                               -10.5                                  0   \\n\",\n       \"\\n\",\n       \"   bureau_balance_MONTHS_BALANCE_min  bureau_balance_MONTHS_BALANCE_sum  \\n\",\n       \"0                                -96                              -4656  \\n\",\n       \"1                                -82                              -3403  \\n\",\n       \"2                                 -3                                 -6  \\n\",\n       \"3                                -18                               -171  \\n\",\n       \"4                                -21                               -231  \"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bureau_balance_agg = agg_numeric(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\\n\",\n    \"bureau_balance_agg.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 按客户对Bureau Balance数据集聚合\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 按贷款对数据集分组\\n\",\n    \"bureau_by_loan = bureau_balance_agg.merge(bureau_balance_counts, right_index = True, left_on = 'SK_ID_BUREAU', how = 'outer')\\n\",\n    \"\\n\",\n    \"# 将SK_ID_CURR结合\\n\",\n    \"bureau_by_loan = bureau[['SK_ID_BUREAU', 'SK_ID_CURR']].merge(bureau_by_loan, on = 'SK_ID_BUREAU', how = 'left')\\n\",\n    \"\\n\",\n    \"# 将每个客户的数据聚合\\n\",\n    \"bureau_balance_by_client = agg_numeric(bureau_by_loan.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'client')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 在训练数据中插入计算处理后的特征\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Original Number of Feature:  122\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"original_features = list(train.columns)\\n\",\n    \"print('Original Number of Feature: ', len(original_features))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 结合Bureau数据集计数（这里有些同学可能会报key error，原因在于没有更新pandas。方法： 命令行输入'pip install -U pandas '）\\n\",\n    \"train = train.merge(bureau_counts, on = 'SK_ID_CURR', how = 'left')\\n\",\n    \"\\n\",\n    \"# 结合Bureau数据集\\n\",\n    \"train = train.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')\\n\",\n    \"\\n\",\n    \"# 按客户对月度信息分组\\n\",\n    \"train = train.merge(bureau_balance_by_client, on = 'SK_ID_CURR', how = 'left')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number of features using previous loans from other institutions data:  333\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"new_features = list(train.columns)\\n\",\n    \"print('Number of features using previous loans from other institutions data: ', len(new_features))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 特征工程结果\\n\",\n    \"在完成上述所有工作之后，现在我们来看看我们所创建的变量。首先可以查看缺失值的百分比、变量与目标的相关性以及变量与其他变量的相关性。变量之间的相关性可以表明是否有共线变量，即彼此高度相关的变量。通常我们要删除一对共线变量中的一个，因为该变量是多余的。我们还可以使用缺失值的百分比来删除没有表征足够信息的一些特征。特征选择是接下来工作的一个重点，通过减少特征的数量，可以帮助模型在训练期间有效学习，并且还可以更好地对测试数据进行信息概括。“维数灾难”是指由太多特征（太高的维度）引起的问题的名称。随着变量数量的增加，学习这些变量和目标值之间的关系所需的数据量呈指数增长\\n\",\n    \"特征选择是删除变量的过程，以帮助我们的模型更好地学习和推广到测试集。目的是去除无用/冗余变量，同时保留有用的变量。有许多工具可用于此过程，但是在本文中，我们将主要删除具有高缺失率和高相关性的变量的列。之后我们可以了解如何使用从诸如梯度增强机或随机森林模型之类的模型中返回的特征来执行特征选择。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 缺失值\\n\",\n    \"主要考虑丢弃高缺失率的列\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 列缺失值计算函数\\n\",\n    \"def missing_values_table(df):\\n\",\n    \"    # 总缺失值\\n\",\n    \"    mis_val = df.isnull().sum()\\n\",\n    \"    \\n\",\n    \"    # 缺失值百分比\\n\",\n    \"    mis_val_percent = 100 * df.isnull().sum() / len(df)\\n\",\n    \"    \\n\",\n    \"    # 对结果建表\\n\",\n    \"    mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)# concat函数可以将数据根据不同的轴作简单的融合连接\\n\",\n    \"    \\n\",\n    \"    # 列重命名\\n\",\n    \"    mis_val_table_ren_columns = mis_val_table.rename(\\n\",\n    \"    columns = {0 : 'Missing Values', 1 : '% of Total Values'})\\n\",\n    \"    \\n\",\n    \"    # 按缺失率递减排序\\n\",\n    \"    mis_val_table_ren_columns = mis_val_table_ren_columns[\\n\",\n    \"        mis_val_table_ren_columns.iloc[:, 1] != 0].sort_values(\\n\",\n    \"    '% of Total Values', ascending=False).round(1)\\n\",\n    \"    \\n\",\n    \"    # 输出总信息\\n\",\n    \"    print (\\\"Your selected dataframe has \\\" + str(df.shape[1]) + \\\" columns.\\\\n\\\"      \\n\",\n    \"            \\\"There are \\\" + str(mis_val_table_ren_columns.shape[0]) +\\n\",\n    \"              \\\" columns that have missing values.\\\")  \\n\",\n    \"    \\n\",\n    \"    # 返回缺失信息\\n\",\n    \"    return mis_val_table_ren_columns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Your selected dataframe has 333 columns.\\n\",\n      \"There are 278 columns that have missing values.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Missing Values</th>\\n\",\n       \"      <th>% of Total Values</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_min</th>\\n\",\n       \"      <td>227502</td>\\n\",\n       \"      <td>74.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_max</th>\\n\",\n       \"      <td>227502</td>\\n\",\n       \"      <td>74.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>bureau_AMT_ANNUITY_mean</th>\\n\",\n       \"      <td>227502</td>\\n\",\n       \"      <td>74.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>client_bureau_balance_STATUS_4_count_min</th>\\n\",\n       \"      <td>215280</td>\\n\",\n       \"      <td>70.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>client_bureau_balance_STATUS_3_count_norm_mean</th>\\n\",\n       \"      <td>215280</td>\\n\",\n       \"      <td>70.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_count_min</th>\\n\",\n       \"      <td>215280</td>\\n\",\n       \"      <td>70.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>client_bureau_balance_STATUS_4_count_max</th>\\n\",\n       \"      <td>215280</td>\\n\",\n       \"      <td>70.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>client_bureau_balance_STATUS_4_count_mean</th>\\n\",\n       \"      <td>215280</td>\\n\",\n       \"      <td>70.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>client_bureau_balance_STATUS_3_count_norm_min</th>\\n\",\n       \"      <td>215280</td>\\n\",\n       \"      <td>70.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>client_bureau_balance_STATUS_3_count_norm_max</th>\\n\",\n       \"      <td>215280</td>\\n\",\n       \"      <td>70.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                Missing Values  \\\\\\n\",\n       \"bureau_AMT_ANNUITY_min                                  227502   \\n\",\n       \"bureau_AMT_ANNUITY_max                                  227502   \\n\",\n       \"bureau_AMT_ANNUITY_mean                                 227502   \\n\",\n       \"client_bureau_balance_STATUS_4_count_min                215280   \\n\",\n       \"client_bureau_balance_STATUS_3_count_norm_mean          215280   \\n\",\n       \"client_bureau_balance_MONTHS_BALANCE_count_min          215280   \\n\",\n       \"client_bureau_balance_STATUS_4_count_max                215280   \\n\",\n       \"client_bureau_balance_STATUS_4_count_mean               215280   \\n\",\n       \"client_bureau_balance_STATUS_3_count_norm_min           215280   \\n\",\n       \"client_bureau_balance_STATUS_3_count_norm_max           215280   \\n\",\n       \"\\n\",\n       \"                                                % of Total Values  \\n\",\n       \"bureau_AMT_ANNUITY_min                                       74.0  \\n\",\n       \"bureau_AMT_ANNUITY_max                                       74.0  \\n\",\n       \"bureau_AMT_ANNUITY_mean                                      74.0  \\n\",\n       \"client_bureau_balance_STATUS_4_count_min                     70.0  \\n\",\n       \"client_bureau_balance_STATUS_3_count_norm_mean               70.0  \\n\",\n       \"client_bureau_balance_MONTHS_BALANCE_count_min               70.0  \\n\",\n       \"client_bureau_balance_STATUS_4_count_max                     70.0  \\n\",\n       \"client_bureau_balance_STATUS_4_count_mean                    70.0  \\n\",\n       \"client_bureau_balance_STATUS_3_count_norm_min                70.0  \\n\",\n       \"client_bureau_balance_STATUS_3_count_norm_max                70.0  \"\n      ]\n     },\n     \"execution_count\": 48,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"missing_train = missing_values_table(train)\\n\",\n    \"missing_train.head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们看到有许多列的缺失值的百分比很高。缺失阈值的选定需要根据问题本身来考虑。这里为了减少特征的数量，我们将删除训练或测试数据中任何大于90%缺失值的列。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"missing_train_vars = list(missing_train.index[missing_train['% of Total Values'] > 90])\\n\",\n    \"len(missing_train_vars)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在删除缺失值之前，首先在测试数据中统计缺失值百分比。然后在训练或测试数据中删除超过90%个缺失值的列。现在重新查看测试数据，执行相同的操作，并查看测试数据中的缺失值。我们已经计算了所有的计数和聚合统计数据，因此只需要将测试数据与适当的数据合并\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 计算测试数据信息\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 读取数据\\n\",\n    \"test = pd.read_csv('application_test.csv')\\n\",\n    \"\\n\",\n    \"# 与bureau_counts数据结合\\n\",\n    \"test = test.merge(bureau_counts, on = 'SK_ID_CURR', how = 'left')\\n\",\n    \"\\n\",\n    \"# 与bbureau_agg数据结合\\n\",\n    \"test = test.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')\\n\",\n    \"\\n\",\n    \"# 与bureau_balance_by_client数据结合\\n\",\n    \"test = test.merge(bureau_balance_by_client, on = 'SK_ID_CURR', how = 'left')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Shape of Testing Data:  (48744, 332)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Shape of Testing Data: ', test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们需要对齐测试和训练数据文件，这意味着需要使它们具有完全相同的列。其实本不需要这样做，但是当进行one-hot编码时，我们需要对齐数据集以确保它们具有相同的列。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_labels = train['TARGET']\\n\",\n    \"\\n\",\n    \"# 通过移除'TARGET'列来对齐列\\n\",\n    \"train, test = train.align(test, join = 'inner', axis = 1)\\n\",\n    \"\\n\",\n    \"train['TARGET'] = train_labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training Data Shape:  (307511, 333)\\n\",\n      \"Testing Data Shape:  (48744, 332)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Training Data Shape: ', train.shape)\\n\",\n    \"print('Testing Data Shape: ', test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在，数据集已经具有相同的列（除了训练数据中的TARGET列），这意味着我们可以在机器学习模型中使用它们，该模型需要在训练和测试数据中看到相同的列\\n\",\n    \"\\n\",\n    \"现在让我们看看测试数据中缺失值的百分比，从而计算出应该丢弃的列\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Your selected dataframe has 332 columns.\\n\",\n      \"There are 275 columns that have missing values.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Missing Values</th>\\n\",\n       \"      <th>% of Total Values</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>COMMONAREA_MEDI</th>\\n\",\n       \"      <td>33495</td>\\n\",\n       \"      <td>68.7</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>COMMONAREA_MODE</th>\\n\",\n       \"      <td>33495</td>\\n\",\n       \"      <td>68.7</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>COMMONAREA_AVG</th>\\n\",\n       \"      <td>33495</td>\\n\",\n       \"      <td>68.7</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>NONLIVINGAPARTMENTS_MEDI</th>\\n\",\n       \"      <td>33347</td>\\n\",\n       \"      <td>68.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>NONLIVINGAPARTMENTS_AVG</th>\\n\",\n       \"      <td>33347</td>\\n\",\n       \"      <td>68.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>NONLIVINGAPARTMENTS_MODE</th>\\n\",\n       \"      <td>33347</td>\\n\",\n       \"      <td>68.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>FONDKAPREMONT_MODE</th>\\n\",\n       \"      <td>32797</td>\\n\",\n       \"      <td>67.3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>LIVINGAPARTMENTS_MEDI</th>\\n\",\n       \"      <td>32780</td>\\n\",\n       \"      <td>67.2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>LIVINGAPARTMENTS_MODE</th>\\n\",\n       \"      <td>32780</td>\\n\",\n       \"      <td>67.2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>LIVINGAPARTMENTS_AVG</th>\\n\",\n       \"      <td>32780</td>\\n\",\n       \"      <td>67.2</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                          Missing Values  % of Total Values\\n\",\n       \"COMMONAREA_MEDI                    33495               68.7\\n\",\n       \"COMMONAREA_MODE                    33495               68.7\\n\",\n       \"COMMONAREA_AVG                     33495               68.7\\n\",\n       \"NONLIVINGAPARTMENTS_MEDI           33347               68.4\\n\",\n       \"NONLIVINGAPARTMENTS_AVG            33347               68.4\\n\",\n       \"NONLIVINGAPARTMENTS_MODE           33347               68.4\\n\",\n       \"FONDKAPREMONT_MODE                 32797               67.3\\n\",\n       \"LIVINGAPARTMENTS_MEDI              32780               67.2\\n\",\n       \"LIVINGAPARTMENTS_MODE              32780               67.2\\n\",\n       \"LIVINGAPARTMENTS_AVG               32780               67.2\"\n      ]\n     },\n     \"execution_count\": 54,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"missing_test = missing_values_table(test)\\n\",\n    \"missing_test.head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0\"\n      ]\n     },\n     \"execution_count\": 55,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"missing_test_vars = list(missing_test.index[missing_test['% of Total Values'] > 90])\\n\",\n    \"len(missing_test_vars)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"There are 0 columns with more than 90% missing in either the training or testing data.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"missing_columns = list(set(missing_test_vars + missing_train_vars))\\n\",\n    \"print('There are %d columns with more than 90%% missing in either the training or testing data.' % len(missing_columns))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 舍弃缺失率高的列\\n\",\n    \"train = train.drop(columns = missing_columns)\\n\",\n    \"test = test.drop(columns = missing_columns)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"本轮中没有舍弃任何列，这是由于每一列的缺失率都低于90%，因此我们考虑使用其他的特征选择方法来减少维数\\n\",\n    \"\\n\",\n    \"此刻我们保存了完整的训练和测试数据。我鼓励大家尝试采用不同的缺失率阈值来删除缺失列并比较结果\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train.to_csv('train_bureau_raw.csv', index = False)\\n\",\n    \"test.to_csv('test_bureau_raw.csv', index = False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 相关性\\n\",\n    \"首先让我们看看变量与目标的相关性。可以从我们创建的任何变量中看到，与训练数据中原有的变量相比，它们具有更大的相关性。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 计算数据集的相关系数\\n\",\n    \"corrs = train.corr()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>TARGET</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>TARGET</th>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_mean</th>\\n\",\n       \"      <td>0.089729</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_min_mean</th>\\n\",\n       \"      <td>0.089038</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>DAYS_BIRTH</th>\\n\",\n       \"      <td>0.078239</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>bureau_CREDIT_ACTIVE_Active_count_norm</th>\\n\",\n       \"      <td>0.077356</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_mean_mean</th>\\n\",\n       \"      <td>0.076424</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_min</th>\\n\",\n       \"      <td>0.075248</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_min_min</th>\\n\",\n       \"      <td>0.073225</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>client_bureau_balance_MONTHS_BALANCE_sum_mean</th>\\n\",\n       \"      <td>0.072606</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\\n\",\n       \"      <td>0.068927</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                  TARGET\\n\",\n       \"TARGET                                          1.000000\\n\",\n       \"bureau_DAYS_CREDIT_mean                         0.089729\\n\",\n       \"client_bureau_balance_MONTHS_BALANCE_min_mean   0.089038\\n\",\n       \"DAYS_BIRTH                                      0.078239\\n\",\n       \"bureau_CREDIT_ACTIVE_Active_count_norm          0.077356\\n\",\n       \"client_bureau_balance_MONTHS_BALANCE_mean_mean  0.076424\\n\",\n       \"bureau_DAYS_CREDIT_min                          0.075248\\n\",\n       \"client_bureau_balance_MONTHS_BALANCE_min_min    0.073225\\n\",\n       \"client_bureau_balance_MONTHS_BALANCE_sum_mean   0.072606\\n\",\n       \"bureau_DAYS_CREDIT_UPDATE_mean                  0.068927\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"corrs = corrs.sort_values('TARGET', ascending = False)\\n\",\n    \"\\n\",\n    \"# 相关性最强的10个属性\\n\",\n    \"pd.DataFrame(corrs['TARGET'].head(10))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"与TARGET相关度最高的变量（除了TARGET本身）是我们创建的变量。然而变量具有高相关性并不意味着它一定是有用的，需要注意的是，如果我们生成了上百个新变量，其中一些变量的相关性可能仅仅是由于随机噪声而产生的\\n\",\n    \"\\n\",\n    \"从怀疑的角度看待相关系数，确实存在有些新创建变量会产生巨大作用。为了评估变量的‘有用性’，我们将查看模型返回的特征重要性。出于好奇（并且因为我们已经编写了函数），所以可以对新创建的变量中的两个进行KDE图展示\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"KeyError\",\n     \"evalue\": \"'client_bureau_balance_counts_mean'\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mKeyError\\u001b[0m                                  Traceback (most recent call last)\",\n      \"\\u001b[1;32mD:\\\\anaconda3\\\\lib\\\\site-packages\\\\pandas\\\\core\\\\indexes\\\\base.py\\u001b[0m in \\u001b[0;36mget_loc\\u001b[1;34m(self, key, method, tolerance)\\u001b[0m\\n\\u001b[0;32m   3077\\u001b[0m             \\u001b[1;32mtry\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 3078\\u001b[1;33m                 \\u001b[1;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_engine\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mget_loc\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mkey\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m   3079\\u001b[0m             \\u001b[1;32mexcept\\u001b[0m \\u001b[0mKeyError\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mpandas\\\\_libs\\\\index.pyx\\u001b[0m in \\u001b[0;36mpandas._libs.index.IndexEngine.get_loc\\u001b[1;34m()\\u001b[0m\\n\",\n      \"\\u001b[1;32mpandas\\\\_libs\\\\index.pyx\\u001b[0m in \\u001b[0;36mpandas._libs.index.IndexEngine.get_loc\\u001b[1;34m()\\u001b[0m\\n\",\n      \"\\u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi\\u001b[0m in \\u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\\u001b[1;34m()\\u001b[0m\\n\",\n      \"\\u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi\\u001b[0m in \\u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\\u001b[1;34m()\\u001b[0m\\n\",\n      \"\\u001b[1;31mKeyError\\u001b[0m: 'client_bureau_balance_counts_mean'\",\n      \"\\nDuring handling of the above exception, another exception occurred:\\n\",\n      \"\\u001b[1;31mKeyError\\u001b[0m                                  Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-61-6f202daac68a>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m \\u001b[0mkde_target\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mvar_name\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;34m'client_bureau_balance_counts_mean'\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdf\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mtrain\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n      \"\\u001b[1;32m<ipython-input-8-10a018c6e757>\\u001b[0m in \\u001b[0;36mkde_target\\u001b[1;34m(var_name, df)\\u001b[0m\\n\\u001b[0;32m      3\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      4\\u001b[0m     \\u001b[1;31m# 计算变量与目标值之间的皮尔森相关系数\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 5\\u001b[1;33m     \\u001b[0mcorr\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mdf\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;34m'TARGET'\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mcorr\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mdf\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[0mvar_name\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      6\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      7\\u001b[0m     \\u001b[1;31m# 计算偿还或未偿还贷款数据的中位数\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mD:\\\\anaconda3\\\\lib\\\\site-packages\\\\pandas\\\\core\\\\frame.py\\u001b[0m in \\u001b[0;36m__getitem__\\u001b[1;34m(self, key)\\u001b[0m\\n\\u001b[0;32m   2686\\u001b[0m             \\u001b[1;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_getitem_multilevel\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mkey\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   2687\\u001b[0m         \\u001b[1;32melse\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 2688\\u001b[1;33m             \\u001b[1;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_getitem_column\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mkey\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m   2689\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   2690\\u001b[0m     \\u001b[1;32mdef\\u001b[0m \\u001b[0m_getitem_column\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mkey\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mD:\\\\anaconda3\\\\lib\\\\site-packages\\\\pandas\\\\core\\\\frame.py\\u001b[0m in \\u001b[0;36m_getitem_column\\u001b[1;34m(self, key)\\u001b[0m\\n\\u001b[0;32m   2693\\u001b[0m         \\u001b[1;31m# get column\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   2694\\u001b[0m         \\u001b[1;32mif\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mcolumns\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mis_unique\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 2695\\u001b[1;33m             \\u001b[1;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_get_item_cache\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mkey\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m   2696\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   2697\\u001b[0m         \\u001b[1;31m# duplicate columns & possible reduce dimensionality\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mD:\\\\anaconda3\\\\lib\\\\site-packages\\\\pandas\\\\core\\\\generic.py\\u001b[0m in \\u001b[0;36m_get_item_cache\\u001b[1;34m(self, item)\\u001b[0m\\n\\u001b[0;32m   2487\\u001b[0m         \\u001b[0mres\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mcache\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   2488\\u001b[0m         \\u001b[1;32mif\\u001b[0m \\u001b[0mres\\u001b[0m \\u001b[1;32mis\\u001b[0m \\u001b[1;32mNone\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 2489\\u001b[1;33m             \\u001b[0mvalues\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_data\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m   2490\\u001b[0m             \\u001b[0mres\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_box_item_values\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mvalues\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   2491\\u001b[0m             \\u001b[0mcache\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[1;33m]\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mres\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mD:\\\\anaconda3\\\\lib\\\\site-packages\\\\pandas\\\\core\\\\internals.py\\u001b[0m in \\u001b[0;36mget\\u001b[1;34m(self, item, fastpath)\\u001b[0m\\n\\u001b[0;32m   4113\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   4114\\u001b[0m             \\u001b[1;32mif\\u001b[0m \\u001b[1;32mnot\\u001b[0m \\u001b[0misna\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 4115\\u001b[1;33m                 \\u001b[0mloc\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mitems\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mget_loc\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m   4116\\u001b[0m             \\u001b[1;32melse\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   4117\\u001b[0m                 \\u001b[0mindexer\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0marange\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mlen\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mitems\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[0misna\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mitems\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mD:\\\\anaconda3\\\\lib\\\\site-packages\\\\pandas\\\\core\\\\indexes\\\\base.py\\u001b[0m in \\u001b[0;36mget_loc\\u001b[1;34m(self, key, method, tolerance)\\u001b[0m\\n\\u001b[0;32m   3078\\u001b[0m                 \\u001b[1;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_engine\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mget_loc\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mkey\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   3079\\u001b[0m             \\u001b[1;32mexcept\\u001b[0m \\u001b[0mKeyError\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 3080\\u001b[1;33m                 \\u001b[1;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_engine\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mget_loc\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_maybe_cast_indexer\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mkey\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m   3081\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   3082\\u001b[0m         \\u001b[0mindexer\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mget_indexer\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[0mkey\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mmethod\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mmethod\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mtolerance\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mtolerance\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mpandas\\\\_libs\\\\index.pyx\\u001b[0m in \\u001b[0;36mpandas._libs.index.IndexEngine.get_loc\\u001b[1;34m()\\u001b[0m\\n\",\n      \"\\u001b[1;32mpandas\\\\_libs\\\\index.pyx\\u001b[0m in \\u001b[0;36mpandas._libs.index.IndexEngine.get_loc\\u001b[1;34m()\\u001b[0m\\n\",\n      \"\\u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi\\u001b[0m in \\u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\\u001b[1;34m()\\u001b[0m\\n\",\n      \"\\u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi\\u001b[0m in \\u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\\u001b[1;34m()\\u001b[0m\\n\",\n      \"\\u001b[1;31mKeyError\\u001b[0m: 'client_bureau_balance_counts_mean'\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"kde_target(var_name='client_bureau_balance_counts_mean', df=train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这个变量代表每个客户每月贷款的平均记录数。例如，如果一个客户以前有三笔贷款，月度数据中的记录数为3、4和5，那么平均记录数的值为4。根据分布，每个贷款的平均月度记录较多的客户更有可能用住房信贷偿还贷款。我们不要过多地研究这个值，但是它可能表明，信用历史更早的客户通常更有可能偿还贷款。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"kde_target(var_name='bureau_CREDIT_ACTIVE_active_counts_norm', df=train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"从分布图可以看出，该变量分布于各个地方。该变量表示以前的贷款的贷款中，'CREADIT_ACTIVE'值为active的数量除以客户以前的贷款总数。这里的相关性太弱了，我认为我们不应该得出任何结论\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 共线变量\\n\",\n    \"我们不仅可以计算变量与目标的相关性，还可以计算每个变量与其他变量的相关性。这将允许我们看看是否存在高度共线变量，这些变量可能应该从数据中删除。\\n\",\n    \"\\n\",\n    \"让我们寻找任何与0.8个变量相关的变量。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 设置阈值\\n\",\n    \"threshold = 0.8\\n\",\n    \"\\n\",\n    \"# 保存相关变量的空字典\\n\",\n    \"above_threshold_vars = {}\\n\",\n    \"\\n\",\n    \"# 对于每一列,记录大于阈值的变量\\n\",\n    \"for col in corrs:\\n\",\n    \"    above_threshold_vars[col] = list(corrs.index[corrs[col] > threshold])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"对于每一对具有高相关性的变量，我们需要去掉其中的一个变量。下面的代码仅通过添加每对共线变量中的其中之一来创建一个变量集合\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number of columns to remove:  134\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 记录需要移除的列和已经检查过的列\\n\",\n    \"cols_to_remove = []\\n\",\n    \"cols_seen = []\\n\",\n    \"cols_to_remove_pair = []\\n\",\n    \"\\n\",\n    \"# 迭代列及相关列\\n\",\n    \"for key, value in above_threshold_vars.items():\\n\",\n    \"    # 查找已经检查过的列\\n\",\n    \"    cols_seen.append(key)\\n\",\n    \"    for x in value:\\n\",\n    \"        if x == key:\\n\",\n    \"            next\\n\",\n    \"        else:\\n\",\n    \"            # 移除一对高相关性变量中的一个\\n\",\n    \"            if x not in cols_seen:\\n\",\n    \"                cols_to_remove.append(x)\\n\",\n    \"                cols_to_remove_pair.append(key)\\n\",\n    \"            \\n\",\n    \"cols_to_remove = list(set(cols_to_remove))\\n\",\n    \"print('Number of columns to remove: ', len(cols_to_remove))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们可以从训练和测试数据集中删除这些列。在删除这些变量之后，我们必须比较删除前后的性能差别\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training Corrs Removed Shape:  (307511, 199)\\n\",\n      \"Testing Corrs Removed Shape:  (48744, 198)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_corrs_removed = train.drop(columns = cols_to_remove)\\n\",\n    \"test_corrs_removed = test.drop(columns = cols_to_remove)\\n\",\n    \"\\n\",\n    \"print('Training Corrs Removed Shape: ', train_corrs_removed.shape)\\n\",\n    \"print('Testing Corrs Removed Shape: ', test_corrs_removed.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_corrs_removed.to_csv('train_bureau_corrs_removed.csv', index = False)\\n\",\n    \"test_corrs_removed.to_csv('test_bureau_corrs_removed.csv', index = False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 建模\\n\",\n    \"为了实际测试这些新数据集的性能，我们将尝试使用它们来进行机器学习！这里，我们将使用我在另一篇笔记中开发的函数来比较这些特性（原始版本和删除的高度相关的变量）。我已经记录了相关效果，下面列出模型控件及两个测试条件：\\n\",\n    \"\\n\",\n    \"对于所有数据集，使用下面所示的模型（用精确的超参数）\\n\",\n    \"\\n\",\n    \"控件：基础数据\\n\",\n    \"测试一：基础数据 + bureau和bureau_balance文件中所有的数据记录的所有数据\\n\",\n    \"测试二：基础数据 + bureau和bureau_balance文件中所有的数据记录的高相关性数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import lightgbm as lgb\\n\",\n    \"\\n\",\n    \"from sklearn.model_selection import KFold\\n\",\n    \"from sklearn.metrics import roc_auc_score\\n\",\n    \"from sklearn.preprocessing import LabelEncoder\\n\",\n    \"\\n\",\n    \"import gc\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def model(features, test_features, encoding = 'ohe', n_folds = 5):\\n\",\n    \"    \\n\",\n    \"    \\\"\\\"\\\"使用基于交叉验证的LightGBM进行训练和测试\\n\",\n    \"    \\n\",\n    \"    参数\\n\",\n    \"    --------\\n\",\n    \"        features (pd.DataFrame): \\n\",\n    \"            用于模型训练的训练数据集，其必须包含'TARGET'列\\n\",\n    \"        test_features (pd.DataFrame): \\n\",\n    \"            用于模型预测的测试数据集 \\n\",\n    \"        encoding (str, default = 'ohe'): \\n\",\n    \"            指定类别变量的编码方式。'oho'：使用one-hot编码。'le'：使用整型标签编码\\n\",\n    \"        n_folds (int, default = 5): \\n\",\n    \"            用于交叉检验的折数，默认采用5折交叉检验\\n\",\n    \"        \\n\",\n    \"    返回值\\n\",\n    \"    --------\\n\",\n    \"        submission (pd.DataFrame): \\n\",\n    \"            包含`SK_ID_CURR`和`TARGET`属性的预测数据\\n\",\n    \"        feature_importances (pd.DataFrame): \\n\",\n    \"            该模型的重要特征\\n\",\n    \"        valid_metrics (pd.DataFrame): \\n\",\n    \"            ROC、AUC等误差统计量（包含训练/测试过程）        \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # 抽取id\\n\",\n    \"    train_ids = features['SK_ID_CURR']\\n\",\n    \"    test_ids = test_features['SK_ID_CURR']\\n\",\n    \"    \\n\",\n    \"    # 抽取标签\\n\",\n    \"    labels = features['TARGET']\\n\",\n    \"    \\n\",\n    \"    # 移除id和标签\\n\",\n    \"    features = features.drop(columns = ['SK_ID_CURR', 'TARGET'])\\n\",\n    \"    test_features = test_features.drop(columns = ['SK_ID_CURR'])\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    # One-Hot编码\\n\",\n    \"    if encoding == 'ohe':\\n\",\n    \"        features = pd.get_dummies(features)\\n\",\n    \"        test_features = pd.get_dummies(test_features)\\n\",\n    \"        \\n\",\n    \"        # 通过列对齐数据帧\\n\",\n    \"        features, test_features = features.align(test_features, join = 'inner', axis = 1)\\n\",\n    \"        \\n\",\n    \"        # No categorical indices to record\\n\",\n    \"        cat_indices = 'auto'\\n\",\n    \"    \\n\",\n    \"    # 整型标签编码\\n\",\n    \"    elif encoding == 'le':\\n\",\n    \"        \\n\",\n    \"        # 创建标签编码器(LabelEncoder)\\n\",\n    \"        label_encoder = LabelEncoder()\\n\",\n    \"        \\n\",\n    \"        # 存储分类索引的列表\\n\",\n    \"        cat_indices = []\\n\",\n    \"        \\n\",\n    \"        # 迭代每一列\\n\",\n    \"        for i, col in enumerate(features):\\n\",\n    \"            if features[col].dtype == 'object':\\n\",\n    \"                # 使用标签编码器将分类特征映射到整型\\n\",\n    \"                features[col] = label_encoder.fit_transform(np.array(features[col].astype(str)).reshape((-1,)))\\n\",\n    \"                test_features[col] = label_encoder.transform(np.array(test_features[col].astype(str)).reshape((-1,)))\\n\",\n    \"\\n\",\n    \"                # 记录分类索引\\n\",\n    \"                cat_indices.append(i)\\n\",\n    \"    \\n\",\n    \"    # 如果标签编码方案无效，则捕获错误\\n\",\n    \"    else:\\n\",\n    \"        raise ValueError(\\\"Encoding must be either 'ohe' or 'le'\\\")\\n\",\n    \"        \\n\",\n    \"    print('Training Data Shape: ', features.shape)\\n\",\n    \"    print('Testing Data Shape: ', test_features.shape)\\n\",\n    \"    \\n\",\n    \"    # 抽取特征名称\\n\",\n    \"    feature_names = list(features.columns)\\n\",\n    \"    \\n\",\n    \"    # 转化为np arrays形式\\n\",\n    \"    features = np.array(features)\\n\",\n    \"    test_features = np.array(test_features)\\n\",\n    \"    \\n\",\n    \"    # 创建k折对象\\n\",\n    \"    k_fold = KFold(n_splits = n_folds, shuffle = False, random_state = 50)\\n\",\n    \"    \\n\",\n    \"    # 特征重要性数组（初始化全为0）\\n\",\n    \"    feature_importance_values = np.zeros(len(feature_names))\\n\",\n    \"    \\n\",\n    \"    # 测试预测性能的数组（初始化全为0）\\n\",\n    \"    test_predictions = np.zeros(test_features.shape[0])\\n\",\n    \"    \\n\",\n    \"    # 保存k折交叉验证输出的数组（初始化全为0）\\n\",\n    \"    out_of_fold = np.zeros(features.shape[0])\\n\",\n    \"    \\n\",\n    \"    # 记录验证和训练分数的列表\\n\",\n    \"    valid_scores = []\\n\",\n    \"    train_scores = []\\n\",\n    \"    \\n\",\n    \"    # 在每一折(k-fold)中迭代\\n\",\n    \"    for train_indices, valid_indices in k_fold.split(features):\\n\",\n    \"        \\n\",\n    \"        # 该折的训练数据\\n\",\n    \"        train_features, train_labels = features[train_indices], labels[train_indices]\\n\",\n    \"        # 该折的验证数据\\n\",\n    \"        valid_features, valid_labels = features[valid_indices], labels[valid_indices]\\n\",\n    \"        \\n\",\n    \"        # 建立模型\\n\",\n    \"        model = lgb.LGBMClassifier(n_estimators=10000, objective = 'binary', \\n\",\n    \"                                   class_weight = 'balanced', learning_rate = 0.05, \\n\",\n    \"                                   reg_alpha = 0.1, reg_lambda = 0.1, \\n\",\n    \"                                   subsample = 0.8, n_jobs = -1, random_state = 50)\\n\",\n    \"        \\n\",\n    \"        # 训练模型\\n\",\n    \"        model.fit(train_features, train_labels, eval_metric = 'auc',\\n\",\n    \"                  eval_set = [(valid_features, valid_labels), (train_features, train_labels)],\\n\",\n    \"                  eval_names = ['valid', 'train'], categorical_feature = cat_indices,\\n\",\n    \"                  early_stopping_rounds = 100, verbose = 200)\\n\",\n    \"        \\n\",\n    \"        # 记录最佳迭代\\n\",\n    \"        best_iteration = model.best_iteration_\\n\",\n    \"        \\n\",\n    \"        # 记录特征重要性\\n\",\n    \"        feature_importance_values += model.feature_importances_ / k_fold.n_splits\\n\",\n    \"        \\n\",\n    \"        # 预测\\n\",\n    \"        test_predictions += model.predict_proba(test_features, num_iteration = best_iteration)[:, 1] / k_fold.n_splits\\n\",\n    \"        \\n\",\n    \"        # 记录折中预测\\n\",\n    \"        out_of_fold[valid_indices] = model.predict_proba(valid_features, num_iteration = best_iteration)[:, 1]\\n\",\n    \"        \\n\",\n    \"        # 记录最佳成绩\\n\",\n    \"        valid_score = model.best_score_['valid']['auc']\\n\",\n    \"        train_score = model.best_score_['train']['auc']\\n\",\n    \"        \\n\",\n    \"        valid_scores.append(valid_score)\\n\",\n    \"        train_scores.append(train_score)\\n\",\n    \"        \\n\",\n    \"        # 清除存储\\n\",\n    \"        gc.enable()\\n\",\n    \"        del model, train_features, valid_features\\n\",\n    \"        gc.collect()\\n\",\n    \"        \\n\",\n    \"    # 显示提交的数据文件\\n\",\n    \"    submission = pd.DataFrame({'SK_ID_CURR': test_ids, 'TARGET': test_predictions})\\n\",\n    \"    \\n\",\n    \"    # 显示重要特征\\n\",\n    \"    feature_importances = pd.DataFrame({'feature': feature_names, 'importance': feature_importance_values})\\n\",\n    \"    \\n\",\n    \"    # 整体验证分数\\n\",\n    \"    valid_auc = roc_auc_score(labels, out_of_fold)\\n\",\n    \"    \\n\",\n    \"    # 将整体验证分数加入数组\\n\",\n    \"    valid_scores.append(valid_auc)\\n\",\n    \"    train_scores.append(np.mean(train_scores))\\n\",\n    \"    \\n\",\n    \"    # 相关数据存储\\n\",\n    \"    fold_names = list(range(n_folds))\\n\",\n    \"    fold_names.append('overall')\\n\",\n    \"    \\n\",\n    \"    # 验证分数数据集\\n\",\n    \"    metrics = pd.DataFrame({'fold': fold_names,\\n\",\n    \"                            'train': train_scores,\\n\",\n    \"                            'valid': valid_scores}) \\n\",\n    \"    \\n\",\n    \"    return submission, feature_importances, metrics\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_feature_importances(df):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    画出模型返回的特征重要性，可以使用于任何的重要性评价标准（默认越高越好）\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        df (dataframe): 特征重要性。必须要有'feature'特征列和'importance'特征重要性列\\n\",\n    \"        \\n\",\n    \"    返回值:\\n\",\n    \"        显示出最重要的15个特征\\n\",\n    \"        \\n\",\n    \"        df (dataframe): 按特征重要性进行排序（从高到低），包括一列标准化后的重要性数值\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # 按特征重要性进行排序\\n\",\n    \"    df = df.sort_values('importance', ascending = False).reset_index()\\n\",\n    \"    \\n\",\n    \"    # 标准化特征重要性\\n\",\n    \"    df['importance_normalized'] = df['importance'] / df['importance'].sum()\\n\",\n    \"\\n\",\n    \"    # 画出直方图\\n\",\n    \"    plt.figure(figsize = (10, 6))\\n\",\n    \"    ax = plt.subplot()\\n\",\n    \"    \\n\",\n    \"    # 反转，将最重要的特征放在最前面\\n\",\n    \"    ax.barh(list(reversed(list(df.index[:15]))), \\n\",\n    \"            df['importance_normalized'].head(15), \\n\",\n    \"            align = 'center', edgecolor = 'k')\\n\",\n    \"    \\n\",\n    \"    # 设置ytick和label\\n\",\n    \"    ax.set_yticks(list(reversed(list(df.index[:15]))))\\n\",\n    \"    ax.set_yticklabels(df['feature'].head(15))\\n\",\n    \"    \\n\",\n    \"    # 标注相关信息\\n\",\n    \"    plt.xlabel('Normalized Importance'); plt.title('Feature Importances')\\n\",\n    \"    plt.show()\\n\",\n    \"    \\n\",\n    \"    return df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 控制(Control)\\n\",\n    \"任何实验的第一步都是建立一个控制(Control)。为此，我们将使用上面定义的函数（实现梯度增强机模型(GBM)）和单个主数据源（application.csv）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_control = pd.read_csv('application_train.csv')\\n\",\n    \"test_control = pd.read_csv('application_test.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"幸运的是，一旦我们花时间编写了一个函数，使用它就非常简单（如果本文有一个中心主题，它就是使用函数来使事情变得更简单和可重用！）上面的函数返回我们可以上传到竞赛的提交数据框架、包含特征重要性的fi数据框架(feature inportance)和具有验证和测试性能的数据框架\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training Data Shape:  (307511, 241)\\n\",\n      \"Testing Data Shape:  (48744, 241)\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.760007\\ttrain's auc: 0.798103\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[269]\\tvalid's auc: 0.760273\\ttrain's auc: 0.809199\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.76114\\ttrain's auc: 0.798328\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[289]\\tvalid's auc: 0.761398\\ttrain's auc: 0.812654\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.750232\\ttrain's auc: 0.79964\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[265]\\tvalid's auc: 0.750451\\ttrain's auc: 0.809734\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.759831\\ttrain's auc: 0.797797\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[282]\\tvalid's auc: 0.760245\\ttrain's auc: 0.811121\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.76071\\ttrain's auc: 0.798106\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[226]\\tvalid's auc: 0.760972\\ttrain's auc: 0.802236\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"submission, fi, metrics = model(train_control, test_control)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>fold</th>\\n\",\n       \"      <th>train</th>\\n\",\n       \"      <th>valid</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.809199</td>\\n\",\n       \"      <td>0.760273</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.812654</td>\\n\",\n       \"      <td>0.761398</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.809734</td>\\n\",\n       \"      <td>0.750451</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.811121</td>\\n\",\n       \"      <td>0.760245</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.802236</td>\\n\",\n       \"      <td>0.760972</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>overall</td>\\n\",\n       \"      <td>0.808989</td>\\n\",\n       \"      <td>0.758635</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      fold     train     valid\\n\",\n       \"0        0  0.809199  0.760273\\n\",\n       \"1        1  0.812654  0.761398\\n\",\n       \"2        2  0.809734  0.750451\\n\",\n       \"3        3  0.811121  0.760245\\n\",\n       \"4        4  0.802236  0.760972\\n\",\n       \"5  overall  0.808989  0.758635\"\n      ]\n     },\n     \"execution_count\": 72,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"metrics\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"因为训练分数高于验证分数，所以控制器稍微有些过拟合，我们可以在后面的正则化部分中解决这个问题（其实我们已经在使用ReGiaLAMBDA、ReGiAlPH以及早停(early stopping)的过程中执行了一些正则化）\\n\",\n    \"\\n\",\n    \"我们可以用另一个函数——plot_feature_importances来可视化特征的重要性。在进行特征选择的时候，特征重要性很可能会有些作用\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x177000bdf28>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fi_sorted = plot_feature_importances(fi)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"submission.to_csv('control.csv', index = False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在比赛中，该控制器的分数为0.745\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 测试一\\n\",\n    \"让我们进行第一次测试。我们只需要把数据传递给函数，函数就完成了我们的大部分工作。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training Data Shape:  (307511, 452)\\n\",\n      \"Testing Data Shape:  (48744, 452)\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.766471\\ttrain's auc: 0.810385\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[293]\\tvalid's auc: 0.767203\\ttrain's auc: 0.827287\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.767523\\ttrain's auc: 0.810288\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[265]\\tvalid's auc: 0.768109\\ttrain's auc: 0.822264\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.761029\\ttrain's auc: 0.811653\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[267]\\tvalid's auc: 0.761625\\ttrain's auc: 0.824147\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.767837\\ttrain's auc: 0.809671\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[236]\\tvalid's auc: 0.76815\\ttrain's auc: 0.816322\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.767516\\ttrain's auc: 0.809788\\n\",\n      \"[400]\\tvalid's auc: 0.767583\\ttrain's auc: 0.842953\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[306]\\tvalid's auc: 0.767902\\ttrain's auc: 0.828518\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"submission_raw, fi_raw, metrics_raw = model(train, test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>fold</th>\\n\",\n       \"      <th>train</th>\\n\",\n       \"      <th>valid</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.827287</td>\\n\",\n       \"      <td>0.767203</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.822264</td>\\n\",\n       \"      <td>0.768109</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.824147</td>\\n\",\n       \"      <td>0.761625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.816322</td>\\n\",\n       \"      <td>0.768150</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.828518</td>\\n\",\n       \"      <td>0.767902</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>overall</td>\\n\",\n       \"      <td>0.823708</td>\\n\",\n       \"      <td>0.766597</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      fold     train     valid\\n\",\n       \"0        0  0.827287  0.767203\\n\",\n       \"1        1  0.822264  0.768109\\n\",\n       \"2        2  0.824147  0.761625\\n\",\n       \"3        3  0.816322  0.768150\\n\",\n       \"4        4  0.828518  0.767902\\n\",\n       \"5  overall  0.823708  0.766597\"\n      ]\n     },\n     \"execution_count\": 76,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"metrics_raw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"从数字上看，处理后的特征训练的效果优于原始特征，但是只有把预测数据提交之后，才可以确定这种更好的验证性能是否转移到了测试数据中\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x17701b35ac8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fi_raw_sorted = plot_feature_importances(fi_raw)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过检查特征的重要性发现，我们构造的一些特征是似乎是最重要的。我们可以找出本文所做的最重要的100个特征的百分比。但是，我们不仅仅要与原始的特征进行比较，还需要与hot-encode后的原始特征进行比较。这些已经记录在我们的FI（原始数据）中\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"% of Top 100 Features created from the bureau data = 53.00\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"top_100 = list(fi_raw_sorted['feature'])[:100]\\n\",\n    \"new_features = [x for x in top_100 if x not in list(fi['feature'])]\\n\",\n    \"\\n\",\n    \"print('%% of Top 100 Features created from the bureau data = %d.00' % len(new_features))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"超过一半的重要特征都是由我们创造的！这让我们相信，我们所做的一切努力都是值得的\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"submission_raw.to_csv('test_one.csv', index = False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在比赛中，测试一的分数为0.759\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 测试二\\n\",\n    \"这很容易，所以让我们继续吧！同以前一样，但将高度共线的变量删除\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training Data Shape:  (307511, 318)\\n\",\n      \"Testing Data Shape:  (48744, 318)\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.764122\\ttrain's auc: 0.8063\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[264]\\tvalid's auc: 0.764565\\ttrain's auc: 0.817897\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.7652\\ttrain's auc: 0.806611\\n\",\n      \"[400]\\tvalid's auc: 0.765625\\ttrain's auc: 0.839251\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[379]\\tvalid's auc: 0.765829\\ttrain's auc: 0.836334\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.757092\\ttrain's auc: 0.808063\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[198]\\tvalid's auc: 0.757149\\ttrain's auc: 0.807627\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.764131\\ttrain's auc: 0.806097\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[287]\\tvalid's auc: 0.76481\\ttrain's auc: 0.821551\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"[200]\\tvalid's auc: 0.763964\\ttrain's auc: 0.806256\\n\",\n      \"[400]\\tvalid's auc: 0.763928\\ttrain's auc: 0.838649\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[312]\\tvalid's auc: 0.764449\\ttrain's auc: 0.825298\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"submission_corrs, fi_corrs, metrics_corr = model(train_corrs_removed, test_corrs_removed)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>fold</th>\\n\",\n       \"      <th>train</th>\\n\",\n       \"      <th>valid</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.817897</td>\\n\",\n       \"      <td>0.764565</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.836334</td>\\n\",\n       \"      <td>0.765829</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.807627</td>\\n\",\n       \"      <td>0.757149</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.821551</td>\\n\",\n       \"      <td>0.764810</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.825298</td>\\n\",\n       \"      <td>0.764449</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>overall</td>\\n\",\n       \"      <td>0.821741</td>\\n\",\n       \"      <td>0.763352</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      fold     train     valid\\n\",\n       \"0        0  0.817897  0.764565\\n\",\n       \"1        1  0.836334  0.765829\\n\",\n       \"2        2  0.807627  0.757149\\n\",\n       \"3        3  0.821551  0.764810\\n\",\n       \"4        4  0.825298  0.764449\\n\",\n       \"5  overall  0.821741  0.763352\"\n      ]\n     },\n     \"execution_count\": 82,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"metrics_corr\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这些结果优于之前的控制器，但是略低于原始特征\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x17702236940>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fi_corrs_sorted = plot_feature_importances(fi_corrs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"submission_corrs.to_csv('test_two.csv', index = False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在比赛中，测试二的分数为0.753\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 结果\\n\",\n    \"在完成了所有的工作之后，我们可以说，额外添加的信息确实提高了性能！该模型显然没有优化我们的数据，但相比于原来的数据集，仍然有明显的改善。让我们正式总结一下表现：\\n\",\n    \"\\n\",\n    \"Experiment\\tTrain AUC\\tValidation AUC\\tTest AUC\\n\",\n    \"Control\\t0.815\\t0.760\\t0.745\\n\",\n    \"Test One\\t0.837\\t0.767\\t0.759\\n\",\n    \"Test Two\\t0.826\\t0.765\\t0.753\\n\",\n    \"\\n\",\n    \"我们所有的辛勤工作转化为了一个小改进——0.014。去除高度共线性的变量会稍微降低性能，所以我们要考虑不同的特征选择方法。此外，我们可以说，我们所构建的很多特征包含于模型所判断的最重要的100个特征中\\n\",\n    \"\\n\",\n    \"在这样的比赛中，即使是这种程度的微小提高也足以让我们上升100多位。通过在本文中进行许多小的改进，我们可以逐步实现更好的性能。我鼓励其他人利用这里的结果来改进自己的代码，我将继续记录我帮助他人的步骤\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 下一步\\n\",\n    \"下一步，我们会将本文中开发的代码应用到其他数据集上。在我们的模型中还有4个其他的数据文件！在下一章中，我们将把这些其他数据文件（其中包含有关以前在家庭信贷贷款的信息）中的信息合并到我们的训练数据中。然后，我们可以建立相同的模型，并运行更多的实验来确定我们的特征工程的效果。在这场比赛中还有很多工作要做，还有更多的成绩要做！我们会在下一章中再见\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "docs/Kaggle/competitions/featured/home-credit-default-risk/README.md",
    "content": "# **[Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk)**\n\n`你能预测每个申请人偿还贷款的能力吗？`\n\n## 比赛说明\n\n由于信用记录不足或根本不存在，许多人很难获得贷款。而且，不幸的是，这些人口往往被不值得信任的贷款人利用。\n\n![](/img/competitions/featured/home-credit-default-risk/about-us-home-credit.jpg)\n\n家庭信贷集团\n\n[Home Credit](http://www.homecredit.net) 通过提供积极和安全的借贷经验，努力扩大无银行账户人口的金融包容性。为了确保这些服务不足的人口具有积极的贷款经验，Home Credit利用各种替代数据 - 包括电信和交易信息 - 来预测其客户的还款能力。\n\n虽然Home Credit目前正在使用各种统计和机器学习方法来做出这些预测，但他们正在挑战Kagglers帮助他们释放数据的全部潜力。这样做将确保能够偿还的客户不会被拒绝，并且贷款将以本金，到期日和还款日历提供，这将使客户获得成功。\n\n> 注意：[项目规范](/docs/kaggle-quickstart.md)\n\n## 参赛成员\n\n* [#黄花鱼]()\n\n> [项目地址: Home Credit Default Risk](/competitions/featured/home-credit-default-risk/ManualFeatureEngineering_P1.ipynb)\n\n... 剩下后期更新\n"
  },
  {
    "path": "docs/Kaggle/competitions/featured/mercari-price-suggestion-challenge/README.md",
    "content": "# **Mercari 价格建议挑战赛**\n\n`你能自动建议网上卖家的产品价格？`\n\n## 比赛说明\n\n可能很难知道有多少东西是真正值得的。小细节可能意味着定价的巨大差异。例如，这些毛衫中的一件成本为335美元，另一件成本为9.99美元。你能猜出哪一个是哪个？\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/mercari_comparison.png)\n\n考虑到网上销售的产品有多少，产品定价的难度就更大了。服装具有强劲的季节性价格趋势，受品牌影响很大，而电子产品则根据产品规格波动。\n\n日本最大的社区购物应用程序Mercari深知这个问题。他们想为卖家提供定价建议，但是这样做很困难，因为卖家可以在Mercari的市场上投入任何东西或任何东西。\n\n在这场比赛中，Mercari挑战你建立一个算法，自动建议正确的产品价格。您将获得用户输入的产品文本说明，包括产品类别名称，品牌名称和项目条件等详细信息。\n\n请注意，由于这些数据的公共性质，这个竞赛是一个“Kernels Only”竞赛。在挑战的第二阶段，文件只能通过内核获得，并且您将无法修改您的方法来响应新数据。在 [数据选项卡](https://www.kaggle.com/c/mercari-price-suggestion-challenge/data) 和 [内核常见问题页面](https://www.kaggle.com/c/mercari-price-suggestion-challenge#Kernels-FAQ) 阅读更多详细信息。\n\n> 注意：[项目规范](/docs/kaggle-quickstart.md)\n\n## 成员角色\n\n| 角色 | 用户 | QQ | GitHub | 负责内容 | 进度 |\n| :--: | :--: | :--: | :--: | :--: | :--: |\n| 发起人 | [片刻](http://cwiki.apachecn.org/display/~jiangzhonglian) | 529815144 |https://github.com/jiangzhonglian | 负责整个项目推进 | 5% |\n| 负责人<br />1.目标定义 | [昵称](ApacheCN Cwiki地址) | xxx-可以选择匿名 | | 负责某个业务理解和指标确认 |  |\n| 参与人<br />1.目标定义 | [昵称](ApacheCN Cwiki地址) | xxx-可以选择匿名 | | 负责某个业务理解和指标确认 |  |\n| xx人<br />2.数据采集 | [昵称](ApacheCN Cwiki地址) | xxx-可以选择匿名 | | 负责 |  |\n| 参与人<br />3.数据整理 | 佳乐 | 872520333| | 负责（助手） | |\n| 参与人<br />3.数据整理 | 诺木人 |498744838| https://github.com/1mrliu| 负责（助手） | |\n| xx人<br />4.构建模型 | [昵称](ApacheCN Cwiki地址) | xxx-可以选择匿名 | | 负责 |  |\n| 参与人<br />4.构建模型 |/ | 610395649 |https://github.com/lai-bluejay | 负责（助手） |  |\n| xx人<br />5.模型评估 | [昵称](ApacheCN Cwiki地址) | xxx-可以选择匿名 | | 负责 |  |\n| 参与人<br />5.模型评估 | / | 610395649 |https://github.com/lai-bluejay | 负责（助手） |   |\n| xx人<br />6.模型发布 | [昵称](ApacheCN Cwiki地址) | xxx-可以选择匿名 | | 负责 |  |\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/project_process.jpg)\n\n## 1.目标定义\n\n* 任务理解\n* 指标确定\n\n## 2.数据采集\n\n* 建模抽样\n* 质量把控\n\n## 3.数据整理\n\n* 数据探索\n* 数据清洗\n* 数据集成\n* 数据变换\n* 数据规约\n\n## 4.构建模型\n\n* 模式发现\n* 构建模型\n* 验证模型\n\n## 5.模型评估\n\n### 设定评估指标\n\n> 1.绝对误差于相对误差\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/EvaluationCriteria/15160975410546.jpg)\n\n> 2.平均绝对误差\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/EvaluationCriteria/15160975616578.jpg)\n\n> 3.均方误差\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/EvaluationCriteria/15160975799651.jpg)\n\n> 4.均方根误差\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/EvaluationCriteria/15160977834397.jpg)\n\n> 5.平均绝对百分误差\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/EvaluationCriteria/15160979383193.jpg)\n\n> 6.Kappa统计\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/EvaluationCriteria/15160982666965.jpg)\n\n> 7.识别准确度（正确率）\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/EvaluationCriteria/15160988495710.jpg)\n\n> 8.识别精确度（ P值 ）\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/EvaluationCriteria/15160989546724.jpg)\n\n> 9.反馈率（ R值 ）\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/EvaluationCriteria/15160989672539.jpg)\n\n> 10.非均衡问题（ F值 ）\n\n\n> 11.ROC曲线/AUC面积\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/EvaluationCriteria/15160989939052.jpg)\n\n> 12.混淆矩阵\n\n![](/img/competitions/featured/mercari-price-suggestion-challenge/EvaluationCriteria/15160990207804.jpg)\n\n* 多模型对比\n* 模型优化\n\n\n\n## 6.模型发布\n    \n* 模型部署\n* 模型重构\n"
  },
  {
    "path": "docs/Kaggle/competitions/featured/mercari-price-suggestion-challenge/mercari_price_notebook.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Introduction\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在这场比赛中，我们被要求预测一个商品销售的价格，所以这是一个回归问题\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np # linear algebra\\n\",\n    \"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from wordcloud import WordCloud\\n\",\n    \"from sklearn.feature_extraction.text import TfidfVectorizer\\n\",\n    \"import string\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train = pd.read_table('train.tsv', engine='c')\\n\",\n    \"test = pd.read_table('test.tsv', engine='c')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 导入数据观看数据的维度,是否有缺失值,特征之间的关联度等等\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>train_id</th>\\n\",\n       \"      <th>name</th>\\n\",\n       \"      <th>item_condition_id</th>\\n\",\n       \"      <th>category_name</th>\\n\",\n       \"      <th>brand_name</th>\\n\",\n       \"      <th>price</th>\\n\",\n       \"      <th>shipping</th>\\n\",\n       \"      <th>item_description</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>MLB Cincinnati Reds T Shirt Size XL</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Men/Tops/T-shirts</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>10.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>No description yet</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Razer BlackWidow Chroma Keyboard</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Electronics/Computers &amp; Tablets/Components &amp; P...</td>\\n\",\n       \"      <td>Razer</td>\\n\",\n       \"      <td>52.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>This keyboard is in great condition and works ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>AVA-VIV Blouse</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Women/Tops &amp; Blouses/Blouse</td>\\n\",\n       \"      <td>Target</td>\\n\",\n       \"      <td>10.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Adorable top with a hint of lace and a key hol...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Leather Horse Statues</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Home/Home Décor/Home Décor Accents</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>New with tags. Leather horses. Retail for [rm]...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>24K GOLD plated rose</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Women/Jewelry/Necklaces</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>44.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Complete with certificate of authenticity</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   train_id                                 name  item_condition_id  \\\\\\n\",\n       \"0         0  MLB Cincinnati Reds T Shirt Size XL                  3   \\n\",\n       \"1         1     Razer BlackWidow Chroma Keyboard                  3   \\n\",\n       \"2         2                       AVA-VIV Blouse                  1   \\n\",\n       \"3         3                Leather Horse Statues                  1   \\n\",\n       \"4         4                 24K GOLD plated rose                  1   \\n\",\n       \"\\n\",\n       \"                                       category_name brand_name  price  \\\\\\n\",\n       \"0                                  Men/Tops/T-shirts        NaN   10.0   \\n\",\n       \"1  Electronics/Computers & Tablets/Components & P...      Razer   52.0   \\n\",\n       \"2                        Women/Tops & Blouses/Blouse     Target   10.0   \\n\",\n       \"3                 Home/Home Décor/Home Décor Accents        NaN   35.0   \\n\",\n       \"4                            Women/Jewelry/Necklaces        NaN   44.0   \\n\",\n       \"\\n\",\n       \"   shipping                                   item_description  \\n\",\n       \"0         1                                 No description yet  \\n\",\n       \"1         0  This keyboard is in great condition and works ...  \\n\",\n       \"2         1  Adorable top with a hint of lace and a key hol...  \\n\",\n       \"3         1  New with tags. Leather horses. Retail for [rm]...  \\n\",\n       \"4         0          Complete with certificate of authenticity  \"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 查看数据的维度\\n\",\n    \"train_id 是数据id  \\n\",\n    \"name 是商品名  \\n\",\n    \"item_condition_id 是卖方提供的物品状况  \\n\",\n    \"category_name 是商品类别  \\n\",\n    \"brand_name 是品牌  \\n\",\n    \"price 是价格也就是我们要预测的y  \\n\",\n    \"shipping 是是否包邮  \\n\",\n    \"item_description 是买家评价  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"训练集大小:(1482535, 8)\\n\",\n      \"测试集大小:(693359, 7)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('训练集大小:{}\\\\n测试集大小:{}'.format(train.shape, test.shape))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 目标分布\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb361a39358>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(20, 15))\\n\",\n    \"plt.hist(train['price'], bins=50, range=[0,250], label='price')\\n\",\n    \"plt.title('Train \\\"price\\\" distribution', fontsize=15)\\n\",\n    \"plt.xlabel('Price', fontsize=15)\\n\",\n    \"plt.ylabel('Samples', fontsize=15)\\n\",\n    \"plt.xticks(fontsize=15)\\n\",\n    \"plt.yticks(fontsize=15)\\n\",\n    \"plt.legend(fontsize=15)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 目标信息\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"count    1.482535e+06\\n\",\n       \"mean     2.673752e+01\\n\",\n       \"std      3.858607e+01\\n\",\n       \"min      0.000000e+00\\n\",\n       \"25%      1.000000e+01\\n\",\n       \"50%      1.700000e+01\\n\",\n       \"75%      2.900000e+01\\n\",\n       \"max      2.009000e+03\\n\",\n       \"Name: price, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train['price'].describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们看到价格的均值在26.7左右,但是也有像2009那样的最大值,变量分布左偏待会所以让我们对价格进行对数转换\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb3383ee470>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(20, 15))\\n\",\n    \"bins=50\\n\",\n    \"plt.hist(train[train['shipping']==1]['price'], bins, normed=True, range=[0,250],\\n\",\n    \"         alpha=0.6, label='price when shipping==1')\\n\",\n    \"plt.hist(train[train['shipping']==0]['price'], bins, normed=True, range=[0,250],\\n\",\n    \"         alpha=0.6, label='price when shipping==0')\\n\",\n    \"plt.title('Train price over shipping type distribution', fontsize=15)\\n\",\n    \"plt.xlabel('Price', fontsize=15)\\n\",\n    \"plt.ylabel('Normalized Samples', fontsize=15)\\n\",\n    \"plt.legend(fontsize=15)\\n\",\n    \"plt.xticks(fontsize=15)\\n\",\n    \"plt.yticks(fontsize=15)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们发现发货是否包邮,并没有将价格分开.但这并不带表这个特征没有用处\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 填充缺失值\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这里用log1p来处理目标y是因为防止出现0和负数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y_train = np.log1p(train['price'])\\n\",\n    \"train['category_name'] = train['category_name'].fillna('Other').astype(str)\\n\",\n    \"train['brand_name'] = train['brand_name'].fillna('missing').astype(str)\\n\",\n    \"train['shipping'] = train['shipping'].astype(str)\\n\",\n    \"train['item_condition_id'] = train['item_condition_id'].astype(str)\\n\",\n    \"train['item_description'] = train['item_description'].fillna('None')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 文本哑变量化\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.pipeline import FeatureUnion\\n\",\n    \"from sklearn.feature_extraction.text import CountVectorizer\\n\",\n    \"default_preprocessor = CountVectorizer().build_preprocessor()\\n\",\n    \"def build_preprocessor(field):\\n\",\n    \"    field_idx = list(train.columns).index(field)\\n\",\n    \"    return lambda x: default_preprocessor(x[field_idx])\\n\",\n    \"    \\n\",\n    \"vectorizer = FeatureUnion([\\n\",\n    \"    ('name', CountVectorizer(\\n\",\n    \"        ngram_range=(1, 2),\\n\",\n    \"        max_features=5000,\\n\",\n    \"        preprocessor=build_preprocessor('name'))),\\n\",\n    \"    ('category_name', CountVectorizer(\\n\",\n    \"        token_pattern='.+',\\n\",\n    \"        preprocessor=build_preprocessor('category_name'))),\\n\",\n    \"    ('brand_name', CountVectorizer(\\n\",\n    \"        token_pattern='.+',\\n\",\n    \"        preprocessor=build_preprocessor('brand_name'))),\\n\",\n    \"    ('shipping', CountVectorizer(\\n\",\n    \"        token_pattern='\\\\d+',\\n\",\n    \"        preprocessor=build_preprocessor('shipping'))),\\n\",\n    \"    ('item_condition_id', CountVectorizer(\\n\",\n    \"        token_pattern='\\\\d+',\\n\",\n    \"        preprocessor=build_preprocessor('item_condition_id'))),\\n\",\n    \"    ('item_description', TfidfVectorizer(\\n\",\n    \"        ngram_range=(1, 3),\\n\",\n    \"        max_features=5000,\\n\",\n    \"        preprocessor=build_preprocessor('item_description'))),\\n\",\n    \"])\\n\",\n    \"X_train = vectorizer.fit_transform(train.values)\\n\",\n    \"X_train\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 利用ridge算法进行预测\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_rmsle(y_true, y_pred):\\n\",\n    \"    return np.sqrt(mean_squared_log_error(np.expm1(y_true), np.expm1(y_pred)))\\n\",\n    \"\\n\",\n    \"cv = KFold(n_splits=10, shuffle=True, random_state=42)\\n\",\n    \"for train_ids, valid_ids in cv.split(X_train):\\n\",\n    \"    model = Ridge(\\n\",\n    \"        solver='auto',\\n\",\n    \"        fit_intercept=True,\\n\",\n    \"        alpha=0.5,\\n\",\n    \"        max_iter=100,\\n\",\n    \"        normalize=False,\\n\",\n    \"        tol=0.05)\\n\",\n    \"    model.fit(X_train[train_ids], y_train[train_ids])\\n\",\n    \"    y_pred_valid = model.predict(X_train[valid_ids])\\n\",\n    \"    rmsle = get_rmsle(y_pred_valid, y_train[valid_ids])\\n\",\n    \"    print(f'valid rmsle: {rmsle:.5f}')\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 算法精确准确率并不高, 我们可以调高 在文本哑变量化中TfidfVectorizer 的max_feature  参数的值\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 单个ridge模型的准确率不行这时我们就可以用lightgbm了.进行多个模型组合具体可以看代码文件\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/digit-recognizer/README.md",
    "content": "# **数字识别**\n\n![](/img/competitions/getting-started/digit-recognizer/front_page.png)\n\n## 比赛说明\n\n* MNIST（\"修改后的国家标准与技术研究所\"）是计算机视觉的事实上的 \"hello world\" 数据集。自1999年发布以来，手写图像的经典数据集已成为基准分类算法的基础。随着新机器学习技术的出现，MNIST 仍然是研究人员和学习者的可靠资源。\n* 在本次比赛中，您的目标是正确 [识别数以万计手写图像的数字](https://www.kaggle.com/c/digit-recognizer) 。我们策划了一套教程式的内核，涵盖从回归到神经网络的一切。我们鼓励您尝试使用不同的算法来学习第一手什么是有效的，以及技术如何比较。\n\n> 注意：[项目规范](/docs/kaggle-quickstart.md)\n\n## 成员角色\n\n| 角色 | 用户 | 内容 | 代码 |\n| -- | -- | -- | -- |\n| 负责: knn | [诺木人](https://github.com/1mrliu) | [knn项目文档](/competitions/getting-started/digit-recognizer/knn算法描述.md) | [knn项目代码](/src/py3.x/kaggle/getting-started/digit-recognizer/knn-python3.6.py) |\n| 负责: svm | [马小穆](https://github.com/maxiaomu) | [svm项目文档](/competitions/getting-started/digit-recognizer/svm算法描述.md) | [svm项目代码](/src/py3.x/kaggle/getting-started/digit-recognizer/svm-python3.6.py) |\n| 负责: 随机森林 | [平淡的天](https://github.com/friedhelm739) | [随机森林项目文档](/competitions/getting-started/digit-recognizer/随机森林算法描述.md) | [随机森林项目代码](/src/py3.x/kaggle/getting-started/digit-recognizer/rf-python3.6.py) |\n| 负责: 神经网络 | [平淡的天](https://github.com/friedhelm739) | [神经网络项目文档](/competitions/getting-started/digit-recognizer/神经网络算法描述.md) | [神经网络项目代码](/src/py3.x/kaggle/getting-started/digit-recognizer/nn-python3.6.py) |\n| 负责: cnn | [==](https://github.com/xiaomingnio) | [cnn项目文档](/competitions/getting-started/digit-recognizer/cnn算法描述.md) | [cnn项目代码](/src/py3.x/kaggle/getting-started/digit-recognizer/keras_cnn-python3.6.py) |\n\n> 数字识别 第一期(2018-04-18)\n\n| 组长 | 组员 | 组员 | 组员 | 组员 | 组员 | 组员 |\n| -- | -- | -- | -- | -- | -- | -- |\n| [限定心态](https://github.com/island99) | [strengthen](https://github.com/Yestrengthen) | [VS53MV](https://github.com/La-fe) | [不会修电脑](https://github.com/smallsmallwood) | [远心](https://github.com/SwordFaith) | [小耀哥_0011](https://github.com/yirenrumeng) | [丨](https://github.com/nincro)|\n\n> 数字识别 第二期(2018-04-21)\n\n| 组长 | 组员 | 组员 | 组员 | 组员 | 组员 |\n| -- | -- | -- | -- | -- | -- |\n|[凌少skier](https://github.com/skierlin)|[Blue]() |[Max]()|[考拉]()|[Happyorg]()|[过客]()|\n\n> 数字识别 第三期(2018-05-03)\n\n| 负责人 | 组员 | 组员|\n| -- | -- | -- |\n|[技术负责人-诺木人](https://github.com/1mrliu)<br/>[辅助负责人-平淡的心]()<br/>[辅助负责人-张凯]()<br/>[活动发起人-片刻](https://github.com/jiangzhonglian)| [ifeng](https://github.com/ifeng2025)<br/>[draw](https://github.com/)<br/>[Faith](https://github.com/77qingliu)<br/>[ggggggggo](https://github.com/)<br/>[嘿！漆漆](https://github.com/77const)<br/>[kickfilper](https://github.com/yongfegnyan)<br/>[Lucien Chen](https://github.com/hubeihubei)<br/>[L~Q~W](https://github.com/)<br/>[琴剑蓝天](https://github.com/xvjie)<br/>[時間ｄāń漠](https://github.com/)<br/>[歲寒✅已认证](https://github.com/)<br/>[給力小青年](https://github.com/XCWDSG)<br/>[星尘](https://github.com/wilderchen)<br/>|[瑛瑛wang](https://github.com/)<br/>[有一个人很酷](https://github.com/)<br/>[静水流深](https://github.com/)<br/>[♡稳稳的幸福](https://github.com/patrickwangqy)<br/>[Verestràsz](https://github.com/soarchorale)<br/>[vslyu](https://github.com/)<br/>[:)]()<br/>[菠菜](https://github.com/wpbshine)<br/>[QQ小冰](https://github.com/Luyu-Liam)<br/>[浅紫色](https://github.com/MarkerGo)<br/>[R](https://github.com/rjdCS)<br/>[ROOT](https://github.com/zch765536121) |\n\n> 数字识别 第四期(2018-05-08)\n\n| 负责人 | 组员 | 组员 | 组员 |\n| :--: | :--: | :--: | :--: |\n|[技术负责人-诺木人](https://github.com/1mrliu)<br>[辅助负责人-BrianCai](https://github.com/BrianCai)<br> [辅助负责人-嘿！漆漆](https://github.com/77const)<br> | [兰博归来](https://github.com/lanboguilai)<br/>[柳生](https://github.com/liushengxu)<br/>[ZARD Forever](https://github.com/boonguan)<br/>[你别理我我没网](https://github.com/framelove)<br/>[666](https://github.com/xuanbabybaby)<br/>[黄蛟](https://github.com/jhuang111)<br/>[冬冬](https://github.com/swdmike)<br/>[荼蘼](https://github.com/mile)<br/>[烁今](https://github.com/guessay) | [简雨](https://github.com/pengyuanqiuqiu)<br/>[B0lt1st](https://github.com/B0lt1st)<br/>[nickine](https://github.com/nickninth)<br/>[dying in the sun](https://github.com/jialindeng)<br/>[王琪琪]()<br/>[常想一二]()<br/>[以朱代墨]()<br/>[Mang0](0xMJ)<br/>[TonyZERO]()<br/> | [冰花小子]()<br/>[阿铮]()<br/>[zh哲]()<br/>[小菜鸡]()<br/>[电酱prpr]()<br/>[琉璃火]()<br/>[张假飞]()<br/>[HAN Shuai](https://github.com/rudyhan)<br/>[有人@我]()<br/>[天儿](https://github.com/smilesboy)<br/>[Jaybo](https://github.com/strivebo)<br/> |\n\n## 开发流程\n\n* 分类问题：0～9 数字\n* 常用算法：knn、决策树、朴素贝叶斯、Logistic回归、SVM、集成方法（随机森林和 AdaBoost）\n\n```\n步骤:\n一. 数据分析\n1. 下载并加载数据\n2. 总体预览数据:了解每列数据的含义,数据的格式等\n3. 数据初步分析,使用统计学与绘图: 由于特征没有特殊的含义，不需要过多的细致分析\n\n二. 特征工程\n1.根据业务,常识,以及第二步的数据分析构造特征工程.\n2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)\n\n三. 模型选择\n1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.\n2.比较各个模型的分数,然后取效果较好的模型作为基础模型.\n\n四. 模型融合\n跳过，这个项目的重点是让大家都了解这个kaggle比赛怎么和算法更好的融合在一起。\n\n五. 修改特征和模型参数\n此处不做过多分析，主要是优化各个算法的参数。\n\n* KNN => k值\n* SVM => 惩罚系数，核函数\n* RF => 树个数，树深度，叶子数\n* PCA => 特征数 or 信息熵\n```\n\n## 一. 数据分析\n\n> 数据下载和加载\n\n数据集下载地址：https://www.kaggle.com/c/digit-recognizer/data\n\n```python\nimport os\nimport csv\nimport datetime\nimport numpy as np\nimport pandas as pd\nfrom sklearn.decomposition import PCA\nfrom sklearn.neighbors import KNeighborsClassifier\n\n\ndata_dir = '/opt/data/kaggle/getting-started/digit-recognizer/'\n\n\n# 加载数据\ndef opencsv():\n    # 使用 pandas 打开\n    data = pd.read_csv(os.path.join(data_dir, 'input/train.csv'))\n    data1 = pd.read_csv(os.path.join(data_dir, 'input/test.csv'))\n\n    train_data = data.values[0:, 1:]  # 读入全部训练数据,  [行，列]\n    train_label = data.values[0:, 0]  # 读取列表的第一列\n    test_data = data1.values[0:, 0:]  # 测试全部测试个数据\n    return train_data, train_label, test_data\n\n\n# 加载数据\ntrainData, trainLabel, testData = opencsv()\n```\n\n> 总体预览数据(目标变量+数据特征)\n\n* label: 目标变量（分类标签）\n* pixel0~pixel783: 数据特征（分类属性），特征之间没有特别的业务联系（所以没必要进行统计分析了）\n\n```\nlabel,pixel0,pixel1,pixel2,pixel3,pixel4,pixel5,pixel6,pixel7,pixel8,pixel9,pixel10,pixel11,pixel12,pixel13,pixel14,pixel15,pixel16,pixel17,pixel18,pixel19,pixel20,pixel21,pixel22,pixel23,pixel24,pixel25,pixel26,pixel27,pixel28,pixel29,pixel30,pixel31,pixel32,pixel33,pixel34,pixel35,pixel36,pixel37,pixel38,pixel39,pixel40,pixel41,pixel42,pixel43,pixel44,pixel45,pixel46,pixel47,pixel48,pixel49,pixel50,pixel51,pixel52,pixel53,pixel54,pixel55,pixel56,pixel57,pixel58,pixel59,pixel60,pixel61,pixel62,pixel63,pixel64,pixel65,pixel66,pixel67,pixel68,pixel69,pixel70,pixel71,pixel72,pixel73,pixel74,pixel75,pixel76,pixel77,pixel78,pixel79,pixel80,pixel81,pixel82,pixel83,pixel84,pixel85,pixel86,pixel87,pixel88,pixel89,pixel90,pixel91,pixel92,pixel93,pixel94,pixel95,pixel96,pixel97,pixel98,pixel99,pixel100,pixel101,pixel102,pixel103,pixel104,pixel105,pixel106,pixel107,pixel108,pixel109,pixel110,pixel111,pixel112,pixel113,pixel114,pixel115,pixel116,pixel117,pixel118,pixel119,pixel120,pixel121,pixel122,pixel123,pixel124,pixel125,pixel126,pixel127,pixel128,pixel129,pixel130,pixel131,pixel132,pixel133,pixel134,pixel135,pixel136,pixel137,pixel138,pixel139,pixel140,pixel141,pixel142,pixel143,pixel144,pixel145,pixel146,pixel147,pixel148,pixel149,pixel150,pixel151,pixel152,pixel153,pixel154,pixel155,pixel156,pixel157,pixel158,pixel159,pixel160,pixel161,pixel162,pixel163,pixel164,pixel165,pixel166,pixel167,pixel168,pixel169,pixel170,pixel171,pixel172,pixel173,pixel174,pixel175,pixel176,pixel177,pixel178,pixel179,pixel180,pixel181,pixel182,pixel183,pixel184,pixel185,pixel186,pixel187,pixel188,pixel189,pixel190,pixel191,pixel192,pixel193,pixel194,pixel195,pixel196,pixel197,pixel198,pixel199,pixel200,pixel201,pixel202,pixel203,pixel204,pixel205,pixel206,pixel207,pixel208,pixel209,pixel210,pixel211,pixel212,pixel213,pixel214,pixel215,pixel216,pixel217,pixel218,pixel219,pixel220,pixel221,pixel222,pixel223,pixel224,pixel225,pixel226,pixel227,pixel228,pixel229,pixel230,pixel231,pixel232,pixel233,pixel234,pixel235,pixel236,pixel237,pixel238,pixel239,pixel240,pixel241,pixel242,pixel243,pixel244,pixel245,pixel246,pixel247,pixel248,pixel249,pixel250,pixel251,pixel252,pixel253,pixel254,pixel255,pixel256,pixel257,pixel258,pixel259,pixel260,pixel261,pixel262,pixel263,pixel264,pixel265,pixel266,pixel267,pixel268,pixel269,pixel270,pixel271,pixel272,pixel273,pixel274,pixel275,pixel276,pixel277,pixel278,pixel279,pixel280,pixel281,pixel282,pixel283,pixel284,pixel285,pixel286,pixel287,pixel288,pixel289,pixel290,pixel291,pixel292,pixel293,pixel294,pixel295,pixel296,pixel297,pixel298,pixel299,pixel300,pixel301,pixel302,pixel303,pixel304,pixel305,pixel306,pixel307,pixel308,pixel309,pixel310,pixel311,pixel312,pixel313,pixel314,pixel315,pixel316,pixel317,pixel318,pixel319,pixel320,pixel321,pixel322,pixel323,pixel324,pixel325,pixel326,pixel327,pixel328,pixel329,pixel330,pixel331,pixel332,pixel333,pixel334,pixel335,pixel336,pixel337,pixel338,pixel339,pixel340,pixel341,pixel342,pixel343,pixel344,pixel345,pixel346,pixel347,pixel348,pixel349,pixel350,pixel351,pixel352,pixel353,pixel354,pixel355,pixel356,pixel357,pixel358,pixel359,pixel360,pixel361,pixel362,pixel363,pixel364,pixel365,pixel366,pixel367,pixel368,pixel369,pixel370,pixel371,pixel372,pixel373,pixel374,pixel375,pixel376,pixel377,pixel378,pixel379,pixel380,pixel381,pixel382,pixel383,pixel384,pixel385,pixel386,pixel387,pixel388,pixel389,pixel390,pixel391,pixel392,pixel393,pixel394,pixel395,pixel396,pixel397,pixel398,pixel399,pixel400,pixel401,pixel402,pixel403,pixel404,pixel405,pixel406,pixel407,pixel408,pixel409,pixel410,pixel411,pixel412,pixel413,pixel414,pixel415,pixel416,pixel417,pixel418,pixel419,pixel420,pixel421,pixel422,pixel423,pixel424,pixel425,pixel426,pixel427,pixel428,pixel429,pixel430,pixel431,pixel432,pixel433,pixel434,pixel435,pixel436,pixel437,pixel438,pixel439,pixel440,pixel441,pixel442,pixel443,pixel444,pixel445,pixel446,pixel447,pixel448,pixel449,pixel450,pixel451,pixel452,pixel453,pixel454,pixel455,pixel456,pixel457,pixel458,pixel459,pixel460,pixel461,pixel462,pixel463,pixel464,pixel465,pixel466,pixel467,pixel468,pixel469,pixel470,pixel471,pixel472,pixel473,pixel474,pixel475,pixel476,pixel477,pixel478,pixel479,pixel480,pixel481,pixel482,pixel483,pixel484,pixel485,pixel486,pixel487,pixel488,pixel489,pixel490,pixel491,pixel492,pixel493,pixel494,pixel495,pixel496,pixel497,pixel498,pixel499,pixel500,pixel501,pixel502,pixel503,pixel504,pixel505,pixel506,pixel507,pixel508,pixel509,pixel510,pixel511,pixel512,pixel513,pixel514,pixel515,pixel516,pixel517,pixel518,pixel519,pixel520,pixel521,pixel522,pixel523,pixel524,pixel525,pixel526,pixel527,pixel528,pixel529,pixel530,pixel531,pixel532,pixel533,pixel534,pixel535,pixel536,pixel537,pixel538,pixel539,pixel540,pixel541,pixel542,pixel543,pixel544,pixel545,pixel546,pixel547,pixel548,pixel549,pixel550,pixel551,pixel552,pixel553,pixel554,pixel555,pixel556,pixel557,pixel558,pixel559,pixel560,pixel561,pixel562,pixel563,pixel564,pixel565,pixel566,pixel567,pixel568,pixel569,pixel570,pixel571,pixel572,pixel573,pixel574,pixel575,pixel576,pixel577,pixel578,pixel579,pixel580,pixel581,pixel582,pixel583,pixel584,pixel585,pixel586,pixel587,pixel588,pixel589,pixel590,pixel591,pixel592,pixel593,pixel594,pixel595,pixel596,pixel597,pixel598,pixel599,pixel600,pixel601,pixel602,pixel603,pixel604,pixel605,pixel606,pixel607,pixel608,pixel609,pixel610,pixel611,pixel612,pixel613,pixel614,pixel615,pixel616,pixel617,pixel618,pixel619,pixel620,pixel621,pixel622,pixel623,pixel624,pixel625,pixel626,pixel627,pixel628,pixel629,pixel630,pixel631,pixel632,pixel633,pixel634,pixel635,pixel636,pixel637,pixel638,pixel639,pixel640,pixel641,pixel642,pixel643,pixel644,pixel645,pixel646,pixel647,pixel648,pixel649,pixel650,pixel651,pixel652,pixel653,pixel654,pixel655,pixel656,pixel657,pixel658,pixel659,pixel660,pixel661,pixel662,pixel663,pixel664,pixel665,pixel666,pixel667,pixel668,pixel669,pixel670,pixel671,pixel672,pixel673,pixel674,pixel675,pixel676,pixel677,pixel678,pixel679,pixel680,pixel681,pixel682,pixel683,pixel684,pixel685,pixel686,pixel687,pixel688,pixel689,pixel690,pixel691,pixel692,pixel693,pixel694,pixel695,pixel696,pixel697,pixel698,pixel699,pixel700,pixel701,pixel702,pixel703,pixel704,pixel705,pixel706,pixel707,pixel708,pixel709,pixel710,pixel711,pixel712,pixel713,pixel714,pixel715,pixel716,pixel717,pixel718,pixel719,pixel720,pixel721,pixel722,pixel723,pixel724,pixel725,pixel726,pixel727,pixel728,pixel729,pixel730,pixel731,pixel732,pixel733,pixel734,pixel735,pixel736,pixel737,pixel738,pixel739,pixel740,pixel741,pixel742,pixel743,pixel744,pixel745,pixel746,pixel747,pixel748,pixel749,pixel750,pixel751,pixel752,pixel753,pixel754,pixel755,pixel756,pixel757,pixel758,pixel759,pixel760,pixel761,pixel762,pixel763,pixel764,pixel765,pixel766,pixel767,pixel768,pixel769,pixel770,pixel771,pixel772,pixel773,pixel774,pixel775,pixel776,pixel777,pixel778,pixel779,pixel780,pixel781,pixel782,pixel783\n1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,141,139,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,106,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,185,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,89,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,146,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,156,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,185,255,255,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,185,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,185,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,63,254,254,62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,220,179,6,0,0,0,0,0,0,0,0,9,77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28,247,17,0,0,0,0,0,0,0,0,27,202,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,242,155,0,0,0,0,0,0,0,0,27,254,63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,160,207,6,0,0,0,0,0,0,0,27,254,65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,127,254,21,0,0,0,0,0,0,0,20,239,65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,77,254,21,0,0,0,0,0,0,0,0,195,65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,70,254,21,0,0,0,0,0,0,0,0,195,142,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,56,251,21,0,0,0,0,0,0,0,0,195,227,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,222,153,5,0,0,0,0,0,0,0,120,240,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,67,251,40,0,0,0,0,0,0,0,94,255,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,234,184,0,0,0,0,0,0,0,19,245,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,234,169,0,0,0,0,0,0,0,3,199,182,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,154,205,4,0,0,26,72,128,203,208,254,254,131,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,61,254,129,113,186,245,251,189,75,56,136,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,216,233,233,159,104,52,0,0,0,38,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,206,106,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,186,159,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,209,101,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,25,130,155,254,254,254,157,30,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,103,253,253,253,253,253,253,253,253,114,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,208,253,253,253,253,253,253,253,253,253,253,107,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,31,253,253,253,253,253,253,253,253,253,253,253,215,101,3,0,0,0,0,0,0,0,0,0,0,0,0,23,210,253,253,253,248,161,222,222,246,253,253,253,253,253,39,0,0,0,0,0,0,0,0,0,0,0,0,136,253,253,253,229,77,0,0,0,70,218,253,253,253,253,215,91,0,0,0,0,0,0,0,0,0,0,5,214,253,253,253,195,0,0,0,0,0,104,224,253,253,253,253,215,29,0,0,0,0,0,0,0,0,0,116,253,253,253,247,75,0,0,0,0,0,0,26,200,253,253,253,253,216,4,0,0,0,0,0,0,0,0,254,253,253,253,195,0,0,0,0,0,0,0,0,26,200,253,253,253,253,5,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,25,231,253,253,253,36,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,0,223,253,253,253,129,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,0,127,253,253,253,129,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,0,139,253,253,253,90,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,78,248,253,253,253,5,0,0,0,0,0,0,0,0,254,253,253,253,216,34,0,0,0,0,0,0,0,33,152,253,253,253,107,1,0,0,0,0,0,0,0,0,206,253,253,253,253,140,0,0,0,0,0,30,139,234,253,253,253,154,2,0,0,0,0,0,0,0,0,0,16,205,253,253,253,250,208,106,106,106,200,237,253,253,253,253,209,22,0,0,0,0,0,0,0,0,0,0,0,82,253,253,253,253,253,253,253,253,253,253,253,253,253,209,22,0,0,0,0,0,0,0,0,0,0,0,0,1,91,253,253,253,253,253,253,253,253,253,253,213,90,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,18,129,208,253,253,253,253,159,129,90,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n```\n\n## 二. 特征工程（特征之间关系不大，此步骤跳过）\n\n1. 当然可以做一些归一化的操作\n2. 做降维：因为有些列一直都为了0，信息熵几乎为0，没有使用的必要\n\n```python\n# 数据预处理-降维 PCA主成成分分析\ndef dRPCA(x_train, x_test, COMPONENT_NUM):\n    print('dimensionality reduction...')\n    trainData = np.array(x_train)\n    testData = np.array(x_test)\n    '''\n    使用说明：https://www.cnblogs.com/pinard/p/6243025.html\n    n_components>=1\n      n_components=NUM   设置占特征数量比\n    0 < n_components < 1\n      n_components=0.99  设置阈值总方差占比\n    '''\n    pca = PCA(n_components=COMPONENT_NUM, whiten=False)\n    pca.fit(trainData)  # Fit the model with X\n    pcaTrainData = pca.transform(trainData)  # Fit the model with X and 在X上完成降维.\n    pcaTestData = pca.transform(testData)  # Fit the model with X and 在X上完成降维.\n\n    # pca 方差大小、方差占比、特征数量\n    # print(\"方差大小:\\n\", pca.explained_variance_, \"方差占比:\\n\", pca.explained_variance_ratio_)\n    print(\"特征数量: %s\" % pca.n_components_)\n    print(\"总方差占比: %s\" % sum(pca.explained_variance_ratio_))\n    return pcaTrainData, pcaTestData\n\n\n# 降维处理\ntrainDataPCA, testDataPCA = dRPCA(trainData, testData, 0.8)\n```\n\n## 三. 模型选择\n\n1. 根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.\n2. 比较各个模型的分数,然后取效果较好的模型作为基础模型.\n\n* 分类问题：0～9 数字\n* 常用算法：knn、决策树、朴素贝叶斯、Logistic回归、SVM、集成方法（随机森林和 AdaBoost）\n\n> knn\n\n```python\ndef trainModel(trainData, trainLabel):\n    clf = KNeighborsClassifier()  # default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10)\n    clf.fit(trainData, np.ravel(trainLabel))  # ravel Return a contiguous flattened array.\n    return clf\n\n\n# 模型训练\nclf = trainModel(trainDataPCA, trainLabel)\n# 结果预测\ntestLabel = clf.predict(testDataPCA)\n```\n\n> svm\n\n```python\n# 训练模型\ndef trainModel(trainData, trainLabel):\n    print('Train SVM...')\n    clf = SVC(C=4, kernel='rbf')\n    clf.fit(trainData, trainLabel)  # 训练SVM\n    return clf\n\n\n# 模型训练\nclf = trainModel(trainDataPCA, y_train)\n# 结果预测\ntestLabel = clf.predict(pcaTestData)\n```\n\n> RF - Random Forest\n\n```python\n# 训练模型\ndef trainModel(X_train, y_train):\n    print('Train RF...')\n    clf = RandomForestClassifier(\n        n_estimators=10,\n        max_depth=10,\n        min_samples_split=2,\n        min_samples_leaf=1,\n        random_state=34)\n    clf.fit(X_train, y_train)  # 训练rf\n    return clf\n\n\n# 模型训练\nclf = trainModel(trainDataPCA, y_train)\n# 结果预测\ntestLabel = clf.predict(pcaTestData)\n```\n\n> 结果导出\n\n```python\ndef saveResult(result, csvName):\n    with open(csvName, 'wb') as myFile:\n        myWriter = csv.writer(myFile)\n        myWriter.writerow([\"ImageId\", \"Label\"])\n        index = 0\n        for i in result:\n            tmp = []\n            index = index+1\n            tmp.append(index)\n            # tmp.append(i)\n            tmp.append(int(i))\n            myWriter.writerow(tmp)\n\n# 结果的输出\nsaveResult(testLabel, '/opt/data/kaggle/getting-started/digit-recognizer/output/Result_xxx.csv')\n```\n\n## 四. 模型融合\n\n跳过，这个项目的重点是让大家都了解这个kaggle比赛怎么和算法更好的融合在一起。\n\n## 五. 修改特征和模型参数\n\n此处不做过多分析，主要是优化各个算法的参数。\n\n* [KNN](https://github.com/apachecn/AiLearning/blob/master/docs/ml/2.k-近邻算法.md) => k值\n* [SVM](https://github.com/apachecn/AiLearning/blob/master/docs/ml/6.支持向量机.md) => 惩罚系数，核函数\n* [RF](https://github.com/apachecn/AiLearning/blob/master/docs/ml/7.集成方法-随机森林和AdaBoost.md) => 树个数，树深度，叶子数\n* [PCA](https://github.com/apachecn/AiLearning/blob/master/docs/ml/13.利用PCA来简化数据.md) => 特征数 or 信息熵\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/digit-recognizer/cnn算法描述.md",
    "content": "\n\n# 1.导入需要的库\n## 1.1.导入一些必要的库，如pandas、numpy、matplotlib、sklearn\n## 1.2.导入keras（tensorflow backend），用来搭建神经网络\n\n\n```python\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport seaborn as sns\n#将那些用matplotlib绘制的图显示在页面里而不是弹出一个窗口\n%matplotlib inline   \n\nnp.random.seed(2)\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\nimport itertools\n\nfrom keras.utils.np_utils import to_categorical # 转换成 one-hot-encoding\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\nfrom keras.optimizers import adam, RMSprop\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import ReduceLROnPlateau\n```\n\n    Using TensorFlow backend.\n\n\n# 2.数据准备工作\n## 2.1.导入数据\n\n\n```python\n# Load the data\ntrain = pd.read_csv(r'''/home/cd/kaggle-master/datasets/getting-started/digit-recognizer/input/train.csv''')\ntest = pd.read_csv(r'''/home/cd/kaggle-master/datasets/getting-started/digit-recognizer/input/test.csv''')\n\nX_train = train.values[:,1:]\nY_train = train.values[:,0]\ntest=test.values\n```\n\n## 2.2.标准化\n\n\n```python\n# Normalization\nX_train = X_train / 255.0\ntest = test / 255.0\n```\n\n## 2.3.将数组维度变成（28，28，1）\n### 之前用pandas导入数据的时候会将数据变成一维数组\n\n\n```python\nX_train = X_train.reshape(-1,28,28,1)\ntest = test.reshape(-1,28,28,1)\n```\n\n## 2.4.将标签编码为one-hot编码\n### 如：2 -> [0,0,1,0,0,0,0,0,0,0]            \n###        7 -> [0,0,0,0,0,0,0,1,0,0]\n\n\n\n```python\nY_train = to_categorical(Y_train, num_classes = 10)\n```\n\n## 2.5.将训练集随机划分成训练集和验证集\n### 设置随机数种子\n\n\n```python\nrandom_seed = 2\n```\n\n\n```python\nX_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=random_seed)\n```\n\n# 3.CNN\n## 3.1.定义cnn模型\n### cnn结构为[[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out\n\n\n```python\nmodel = Sequential()\n\nmodel.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n                 activation ='relu', input_shape = (28,28,1)))\nmodel.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n                 activation ='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2)))\nmodel.add(Dropout(0.25))\n\n\nmodel.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n                 activation ='relu'))\nmodel.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n                 activation ='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\nmodel.add(Dropout(0.25))\n\n\nmodel.add(Flatten())\nmodel.add(Dense(256, activation = \"relu\"))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(10, activation = \"softmax\"))\n\n```\n\n## 3.2.设置优化器\n### 优化算法选用自适应学习率算法\n[RMSprop](http://blog.csdn.net/bvl10101111/article/details/72616378)\n### 选择默认参数\n\n\n```python\n# Define the optimizer\noptimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\n```\n\n## 3.3.编译模型\n### 优化算法选择RMSprop\n### 损失函数选择categorical_crossentropy，亦称作多类的对数损失\n### 性能评估方法选择准确率\n\n\n```python\n# Compile the model\nmodel.compile(optimizer = optimizer , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n```\n\n\n```python\nepochs = 30 \nbatch_size = 86\n```\n\n### 设置学习率退火器\n### *当评价指标不在提升时，减少学习率\n### *监测量是val_acc，当3个epoch过去而模型性能不提升时，学习率减少的动作会触发\n### *factor：每次减少学习率的因子，学习率将以lr = lr×factor的形式被减少\n### *min_lr：学习率的下限\n\n### *回调函数是一组在训练的特定阶段被调用的函数集，你可以使用回调函数来观察训练过程中网络内部的状态和统计信息。\n\n\n```python\n# Set a learning rate annealer\nlearning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', \n                                            patience=3, \n                                            verbose=1, \n                                            factor=0.5, \n                                            min_lr=0.00001)\n```\n\n## 3.4.设置图片生成器\n### 用以生成一个batch的图像数据，支持实时数据提升。训练时该函数会无限生成数据，直到达到规定的epoch次数为止。\n\n\n```python\ndatagen = ImageDataGenerator(\n        featurewise_center=False,  # 使输入数据集去中心化（均值为0）, 按feature执行\n        samplewise_center=False,  # 使输入数据的每个样本均值为0\n        featurewise_std_normalization=False,  # 将输入除以数据集的标准差以完成标准化, 按feature执行\n        samplewise_std_normalization=False,  # 将输入的每个样本除以其自身的标准差\n        zca_whitening=False,  # 对输入数据施加ZCA白化\n        rotation_range=10,  # 数据增强时图片随机转动的角度\n        zoom_range = 0.1, # 随机缩放的幅度\n        width_shift_range=0.1,  # 图片宽度的某个比例，数据增强时图片水平偏移的幅度\n        height_shift_range=0.1,  # 图片高度的某个比例，数据增强时图片竖直偏移的幅度\n        horizontal_flip=False,  # 进行随机水平翻转\n        vertical_flip=False)  # 进行随机竖直翻转\n\n```\n\n### 计算依赖于数据的变换所需要的统计信息(均值方差等\n\n\n```python\ndatagen.fit(X_train)\n```\n\n## 3.5.训练模型\n### fit_generator：利用Python的生成器，逐个生成数据的batch并进行训练。生成器与模型将并行执行以提高效率\n### datagen.flow（）：生成器函数，接收numpy数组和标签为参数,生成经过数据增强或标准化后的batch数据,并在一个无限循环中不断的返回batch数据\n### callbacks=[learning_rate_reduction]：回调函数，这个list中的回调函数将会在训练过程中的适当时机被调用\n\n\n\n```python\nimport datetime\nstarttime = datetime.datetime.now()\n\nhistory = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size),\n                              epochs = epochs, validation_data = (X_val,Y_val),\n                              verbose = 2, steps_per_epoch=X_train.shape[0] // batch_size\n                              , callbacks=[learning_rate_reduction])\n\nendtime = datetime.datetime.now()\n\nprint ((endtime - starttime).seconds)\n\n\n```\n\n    Epoch 1/30\n     - 10s - loss: 0.0352 - acc: 0.9900 - val_loss: 0.0174 - val_acc: 0.9943\n    Epoch 2/30\n     - 10s - loss: 0.0322 - acc: 0.9908 - val_loss: 0.0176 - val_acc: 0.9952\n    Epoch 3/30\n     - 10s - loss: 0.0353 - acc: 0.9895 - val_loss: 0.0177 - val_acc: 0.9950\n    Epoch 4/30\n     - 9s - loss: 0.0320 - acc: 0.9908 - val_loss: 0.0172 - val_acc: 0.9950\n    Epoch 5/30\n     - 9s - loss: 0.0350 - acc: 0.9903 - val_loss: 0.0173 - val_acc: 0.9952\n    Epoch 6/30\n    \n    Epoch 00006: reducing learning rate to 1e-05.\n     - 10s - loss: 0.0346 - acc: 0.9897 - val_loss: 0.0170 - val_acc: 0.9948\n    Epoch 7/30\n     - 10s - loss: 0.0350 - acc: 0.9897 - val_loss: 0.0171 - val_acc: 0.9948\n    Epoch 8/30\n     - 10s - loss: 0.0338 - acc: 0.9907 - val_loss: 0.0170 - val_acc: 0.9950\n    Epoch 9/30\n     - 10s - loss: 0.0355 - acc: 0.9896 - val_loss: 0.0172 - val_acc: 0.9948\n    Epoch 10/30\n     - 9s - loss: 0.0335 - acc: 0.9907 - val_loss: 0.0174 - val_acc: 0.9948\n    Epoch 11/30\n     - 9s - loss: 0.0318 - acc: 0.9903 - val_loss: 0.0171 - val_acc: 0.9948\n    Epoch 12/30\n     - 10s - loss: 0.0348 - acc: 0.9902 - val_loss: 0.0173 - val_acc: 0.9948\n    Epoch 13/30\n     - 10s - loss: 0.0337 - acc: 0.9902 - val_loss: 0.0170 - val_acc: 0.9950\n    Epoch 14/30\n     - 10s - loss: 0.0344 - acc: 0.9902 - val_loss: 0.0172 - val_acc: 0.9948\n    Epoch 15/30\n     - 9s - loss: 0.0339 - acc: 0.9900 - val_loss: 0.0171 - val_acc: 0.9950\n    Epoch 16/30\n     - 10s - loss: 0.0338 - acc: 0.9904 - val_loss: 0.0168 - val_acc: 0.9948\n    Epoch 17/30\n     - 10s - loss: 0.0342 - acc: 0.9902 - val_loss: 0.0166 - val_acc: 0.9950\n    Epoch 18/30\n     - 10s - loss: 0.0358 - acc: 0.9903 - val_loss: 0.0169 - val_acc: 0.9950\n    Epoch 19/30\n     - 9s - loss: 0.0339 - acc: 0.9903 - val_loss: 0.0166 - val_acc: 0.9950\n    Epoch 20/30\n     - 10s - loss: 0.0356 - acc: 0.9903 - val_loss: 0.0166 - val_acc: 0.9950\n    Epoch 21/30\n     - 10s - loss: 0.0350 - acc: 0.9900 - val_loss: 0.0165 - val_acc: 0.9952\n    Epoch 22/30\n     - 10s - loss: 0.0350 - acc: 0.9899 - val_loss: 0.0169 - val_acc: 0.9950\n    Epoch 23/30\n     - 10s - loss: 0.0353 - acc: 0.9898 - val_loss: 0.0171 - val_acc: 0.9948\n    Epoch 24/30\n     - 9s - loss: 0.0325 - acc: 0.9904 - val_loss: 0.0167 - val_acc: 0.9948\n    Epoch 25/30\n     - 10s - loss: 0.0359 - acc: 0.9892 - val_loss: 0.0168 - val_acc: 0.9948\n    Epoch 26/30\n     - 10s - loss: 0.0349 - acc: 0.9901 - val_loss: 0.0163 - val_acc: 0.9948\n    Epoch 27/30\n     - 10s - loss: 0.0328 - acc: 0.9908 - val_loss: 0.0166 - val_acc: 0.9948\n    Epoch 28/30\n     - 10s - loss: 0.0331 - acc: 0.9910 - val_loss: 0.0166 - val_acc: 0.9952\n    Epoch 29/30\n     - 10s - loss: 0.0343 - acc: 0.9903 - val_loss: 0.0173 - val_acc: 0.9943\n    Epoch 30/30\n     - 9s - loss: 0.0346 - acc: 0.9902 - val_loss: 0.0166 - val_acc: 0.9948\n    287\n\n\n## 可以看到准确率大概在0.995左右，训练时间为287s (GPU加速后)\n\n### 参考\n[代码](https://www.kaggle.com/yassineghouzam/introduction-to-cnn-keras-0-997-top-6)\n\n\n[keras文档](https://keras-cn.readthedocs.io/en/latest/)\n\n\n### 预测和提交结果\n# predict results\nresults = model.predict(test)\n\n# select the indix with the maximum probability\nresults = np.argmax(results,axis = 1)\n\nresults = pd.Series(results,name=\"Label\")\n\nsubmission = pd.concat([pd.Series(range(1,28001),name = \"ImageId\"),results],axis = 1)\n\nsubmission.to_csv(\"datasets/getting-started/digit-recognizer/ouput/Result_keras_CNN.csv\",index=False)"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/digit-recognizer/knn算法描述.md",
    "content": "# KNN 概述\n\nk-近邻（kNN, k-NearestNeighbor）算法是一种基本分类与回归方法，我们这里只讨论分类问题中的 k-近邻算法。\n\n一句话总结：近朱者赤近墨者黑！\n\nk 近邻算法的输入为实例的特征向量，对应于特征空间的点；输出为实例的类别，可以取多类。k 近邻算法假设给定一个训练数据集，其中的实例类别已定。分类时，对新的实例，根据其 k 个最近邻的训练实例的类别，通过多数表决等方式进行预测。因此，k近邻算法不具有显式的学习过程。\n\nk 近邻算法实际上利用训练数据集对特征向量空间进行划分，并作为其分类的“模型”。 k值的选择、距离度量以及分类决策规则是k近邻算法的三个基本要素。\n\n# KNN场景\n\n电影可以按照题材分类，那么如何区分动作片和爱情片呢？\n\n1. 动作片  打斗次数更多\n2. 爱情片  亲吻次数更多\n\n基于电影中的亲吻、打斗出现的次数，使用 k-近邻算法构造程序，就可以自动划分电影的题材类型。![](/img/competitions/getting-started/digit-recognizer/knn/knn-1-movie.png)现在根据上面我们得到的样本集中所有电影与未知电影的距离，按照距离递增排序，可以找到 k 个距离最近的电影。\n假定 k=3，则三个最靠近的电影依次是， He's Not Really into Dudes 、 Beautiful Woman 和 California Man。\nknn 算法按照距离最近的三部电影的类型，决定未知电影的类型，而这三部电影全是爱情片，因此我们判定未知电影是爱情片。\n\n# KNN原理\n\n## KNN工作原理\n\n1. 假设有一个带有标签的样本数据集（训练样本集），其中包含每条数据与所属分类的对应关系。\n2. 输入没有标签的新数据后，将新数据的每个特征与样本集中数据对应的特征进行比较。\n    * 计算新数据与样本数据集中每条数据的距离。\n    * 对求得的所有距离进行排序（从小到大，越小表示越相似）。\n    * 取前k（k 一般小于等于 20 ）个样本数据对应的分类标签。\n3. 求 k 个数据中出现次数最多的分类标签作为新数据的分类。\n\n## KNN通俗理解\n给定一个训练数据集，对新的输入实例，在训练数据集中找到与该实例最邻近的 k 个实例，这 k 个实例的多数属于某个类，就把该输入实例分为这个类。\n\n## KNN开发流程\n\n```\n收集数据：任何方法\n准备数据：距离计算所需要的数值，最好是结构化的数据格式\n分析数据：任何方法\n训练算法：此步骤不适用于 k-近邻算法\n测试算法：计算错误率\n使用算法：输入样本数据和结构化的输出结果，然后运行 k-近邻算法判断输入数据分类属于哪个分类，最后对计算出的分类执行后续处理\n```\n\n## KNN算法特点\n\n优点：精度高、对异常值不敏感、无数据输入假定\n缺点：计算复杂度高、空间复杂度高\n适用数据范围：数值型和标称型\n\n# KNN项目案例\n\n> 收集数据: 提供文本文件(目标变量+数据特征)\n\n```\n1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,141,139,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,106,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,185,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,89,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,146,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,156,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,185,255,255,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,185,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,185,254,254,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,63,254,254,62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,220,179,6,0,0,0,0,0,0,0,0,9,77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28,247,17,0,0,0,0,0,0,0,0,27,202,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,242,155,0,0,0,0,0,0,0,0,27,254,63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,160,207,6,0,0,0,0,0,0,0,27,254,65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,127,254,21,0,0,0,0,0,0,0,20,239,65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,77,254,21,0,0,0,0,0,0,0,0,195,65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,70,254,21,0,0,0,0,0,0,0,0,195,142,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,56,251,21,0,0,0,0,0,0,0,0,195,227,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,222,153,5,0,0,0,0,0,0,0,120,240,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,67,251,40,0,0,0,0,0,0,0,94,255,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,234,184,0,0,0,0,0,0,0,19,245,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,234,169,0,0,0,0,0,0,0,3,199,182,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,154,205,4,0,0,26,72,128,203,208,254,254,131,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,61,254,129,113,186,245,251,189,75,56,136,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,216,233,233,159,104,52,0,0,0,38,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,206,106,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,186,159,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,209,101,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,25,130,155,254,254,254,157,30,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,103,253,253,253,253,253,253,253,253,114,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,208,253,253,253,253,253,253,253,253,253,253,107,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,31,253,253,253,253,253,253,253,253,253,253,253,215,101,3,0,0,0,0,0,0,0,0,0,0,0,0,23,210,253,253,253,248,161,222,222,246,253,253,253,253,253,39,0,0,0,0,0,0,0,0,0,0,0,0,136,253,253,253,229,77,0,0,0,70,218,253,253,253,253,215,91,0,0,0,0,0,0,0,0,0,0,5,214,253,253,253,195,0,0,0,0,0,104,224,253,253,253,253,215,29,0,0,0,0,0,0,0,0,0,116,253,253,253,247,75,0,0,0,0,0,0,26,200,253,253,253,253,216,4,0,0,0,0,0,0,0,0,254,253,253,253,195,0,0,0,0,0,0,0,0,26,200,253,253,253,253,5,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,25,231,253,253,253,36,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,0,223,253,253,253,129,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,0,127,253,253,253,129,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,0,139,253,253,253,90,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,78,248,253,253,253,5,0,0,0,0,0,0,0,0,254,253,253,253,216,34,0,0,0,0,0,0,0,33,152,253,253,253,107,1,0,0,0,0,0,0,0,0,206,253,253,253,253,140,0,0,0,0,0,30,139,234,253,253,253,154,2,0,0,0,0,0,0,0,0,0,16,205,253,253,253,250,208,106,106,106,200,237,253,253,253,253,209,22,0,0,0,0,0,0,0,0,0,0,0,82,253,253,253,253,253,253,253,253,253,253,253,253,253,209,22,0,0,0,0,0,0,0,0,0,0,0,0,1,91,253,253,253,253,253,253,253,253,253,253,213,90,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,18,129,208,253,253,253,253,159,129,90,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n```\n\n> 准备数据: 分离数据特征和目标变量，加载测试数据\n\n```python\n# 加载数据\ndef opencsv():\n    # 使用 pandas 打开\n    data = pd.read_csv('datasets/getting-started/digit-recognizer/input/train.csv')\n    data1 = pd.read_csv('datasets/getting-started/digit-recognizer/input/test.csv')\n\n    train_data = data.values[0:, 1:]  # 读入全部训练数据\n    train_label = data.values[0:, 0]\n    test_data = data1.values[0:, 0:]  # 测试全部测试个数据\n    return train_data, train_label, test_data\n\ntrainData, trainLabel, testData = opencsv()\n```\n\n> 模型训练: 产生训练模型\n\n```python\n# 模型训练\ndef knnClassify(trainData, trainLabel):\n    knnClf = KNeighborsClassifier()   # default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10)\n    knnClf.fit(trainData, ravel(trainLabel))\n    return knnClf\n\nknnClf = knnClassify(trainData, trainLabel)\n```\n\n> 模型评估: 用于评估结果的正确率和召回率\n\n`暂时没写，后面会进行优化`\n\n> 结果预测: 通过模型来预测新来数据的结果\n\n```python\n# 结果预测\ntestLabel = knnClf.predict(testData)\n```\n\n> 结果导出\n\n```python\ndef saveResult(result, csvName):\n    with open(csvName, 'wb') as myFile:\n        myWriter = csv.writer(myFile)\n        myWriter.writerow([\"ImageId\", \"Label\"])\n        index = 0\n        for i in result:\n            tmp = []\n            index = index+1\n            tmp.append(index)\n            # tmp.append(i)\n            tmp.append(int(i))\n            myWriter.writerow(tmp)\n\n# 结果的输出\nsaveResult(testLabel, 'datasets/getting-started/digit-recognizer/ouput/Result_sklearn_knn.csv')\n```\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/digit-recognizer/svm算法描述.md",
    "content": "# 基于SVM的数字识别算法研究\n\n## SVM 概述\n\n支持向量机(Support Vector Machines, SVM)：是机器学习当中的一种有监督的学习模型，可以应用于求解分类和回归问题。\n\n## SVM 直观认识\n\nreddit上的[Iddo](http://bytesizebio.net/2014/02/05/support-vector-machines-explained-well/)用了一个很好的例子解释了SVM。\n\n\n![explain1](/img/competitions/getting-started/digit-recognizer/svm/explain1.jpg)\n![explain2](/img/competitions/getting-started/digit-recognizer/svm/explain2.jpg)\n    \n对应于SVM来说，这些球叫做 data，棍子叫做 classifie r,最大距离叫做 optimization， 拍桌子叫做 kernelling, 那张纸叫 hyperplane\n\n## SVM 原理\n\n    将上述的直观认识转化为我们最熟悉的数学模型，其实主要内容就是四个部分：\n\n1. SVM的基本原理，从可分到不可分，从线性到非线性\n2. 关于带有约束优化的求解方法：优化拉格朗日乘子法和KKT条件\n3. 核函数的重要意义\n4. 对于优化速度提升的一个重要方法：SMO算法\n    参考下边的几个博客，内容十分详细，然后回顾下边的两张图：由于公式太多，这两张图列出了一些主要的公式，并且按照SVM的求解思想将整个思路串起来。\n\n    ![SVM公式1](/img/competitions/getting-started/digit-recognizer/svm/SVM公式1.jpg)\n    ![SVM公式2](/img/competitions/getting-started/digit-recognizer/svm/SVM公式2.jpg)\n    ![SVM公式3](/img/competitions/getting-started/digit-recognizer/svm/SVM公式3.jpg)\n\n    引用July大神的一句话：“我相信，SVM理解到了一定程度后，是的确能在脑海里从头至尾推导出相关公式的，最初分类函数，最大化分类间隔，max1/||w||，min1/2||w||^2，凸二次规划，拉格朗日函数，转化为对偶问题，SMO算法，都为寻找一个最优解，一个最优分类平面。”\n\n## SVM应用 数字识别\n\n### 数据集的介绍\n\n    在分类研究之前，首先我们需要了解数据的形式和要做的任务：\n        手写数字数据集MNIST(Modified National Institute of Standards and Technology)：该数据集是大约4W多图片和标签的组合，2W多待测图片，图片为28*28像素的灰度图，每个像素点的灰度为0到255，标签为该图片中的数字，为0-9中的一个整数。\n    需要下载的文件：\n* train.csv  训练数据，每行有785列，第一列为标签(label)，之后784*列，每列c为一个像素的灰度值，该像素值对应的坐标为(c/28,c%28)\n* test.csv  需要进行分类的数据，每行有784列，没有第一列标签，其他和训练数据一致\n\n        任务是根据训练集训练好自己的模型，然后利用模型对测试集进行分类，并输出成csv的形式存储起来。\n\n### 实现步骤\n    > 收集数据\n    从kaggle中下载对应的数据集\n    test.csv\n    train.csv\n\n文本文件格式：\n\n```python\n# train.csv\nlabel,pixel0,pixel1,pixel2 ... pixel782,pixel783\n3\t0\t0    0 ... 0\t 0 \n7\t0\t0    0 ... 0\t 0\n2\t0\t0  255 ... 0\t 0\n8\t0\t1   52 ... 0\t 0\n# test.csv\npixel0,pixel1 ... pixel781,pixel782,pixel783\n0\t0  ...\t68\t 0\t 0 \n0\t0  ...\t74\t 55\t 0\n0\t0  ...\t38\t 0\t 0\n0\t1  ...\t0\t 0\t 0\n```\n\n> 准备数据\n\n        这部分主要是加载test.csv和train.csv文件到我们程序当中，通过pandas库文件打开\n\n```python\n# 加载数据\ndef opencsv():\n    print('Load Data...')\n    # 使用 pandas 打开\n    dataTrain = pd.read_csv('datasets/getting-started/digit-recognizer/input/train.csv')\n    dataTest = pd.read_csv('datasets/getting-started/digit-recognizer/input/test.csv')\n    trainData = dataTrain.values[:, 1:]  # 读入全部训练数据\n    trainLabel = dataTrain.values[:, 0]  # 读入对应的第一列的标签\n    testData = dataTest.values[:, :]  # 测试全部测试个数据\n    return trainData, trainLabel, testData\n\n```\n\n> 分析数据: \n\n        对数据的分析是一个特别重要的过程，不仅会让我们对数据集有一个直观的认识，更重要的是为在后续优化当中提供理论依据。\n\n\n```python\n\ndef analyse_data(dataMat):\n    meanVals = np.mean(dataMat, axis=0)\n    meanRemoved = dataMat-meanVals\n    covMat = np.cov(meanRemoved, rowvar=0)\n    eigvals, eigVects = np.linalg.eig(np.mat(covMat))\n    eigValInd = np.argsort(eigvals)\n\n    topNfeat = 100\n    eigValInd = eigValInd[:-(topNfeat+1):-1]\n    cov_all_score = float(sum(eigvals))\n    sum_cov_score = 0\n    for i in range(0, len(eigValInd)):\n        line_cov_score = float(eigvals[eigValInd[i]])\n        sum_cov_score += line_cov_score\n        '''\n        我们发现其中有超过20%的特征值都是0。\n        这就意味着这些特征都是其他特征的副本，也就是说，它们可以通过其他特征来表示，而本身并没有提供额外的信息。\n\n        最前面15个值的数量级大于10^5，实际上那以后的值都变得非常小。\n        这就相当于告诉我们只有部分重要特征，重要特征的数目也很快就会下降。\n\n        最后，我们可能会注意到有一些小的负值，他们主要源自数值误差应该四舍五入成0.\n        '''\n        print('主成分：%s, 方差占比：%s%%, 累积方差占比：%s%%' % (format(i+1, '2.0f'), format(line_cov_score/cov_all_score*100, '4.2f'), format(sum_cov_score/cov_all_score*100, '4.1f')))\n\n```\n        通过对数据的特征分析我们可以知道数据之间存在有很严重的相关性，那么我们可以在分类的过程中考虑提取出重要的特征。\n![数据特征分析](/img/competitions/getting-started/digit-recognizer/svm/数据特征分析.jpg)\n\n```python\ndef dRCsv(x_train, x_test, preData, COMPONENT_NUM):\n    '''\n    x_train:训练集中的0.9作为训练集\n    x_test:训练集中的0.1作为验证集\n    preData:测试集\n    COMPONENT_NUM:保留的特征数\n    '''\n    print('dimensionality reduction...')\n    trainData = np.array(x_train)\n    testData = np.array(x_test)\n    preData = np.array(preData)\n\n    pca = PCA(n_components=COMPONENT_NUM, whiten=True)\n    pca.fit(trainData)  # Fit the model with X\n    pcaTrainData = pca.transform(trainData)  # Fit the model with X and 在X上完成降维.\n    pcaTestData = pca.transform(testData)  # Fit the model with X and 在X上完成降维.\n    pcaPreData = pca.transform(preData)  # Fit the model with X and 在X上完成降维.\n\n    return pcaTrainData,  pcaTestData, pcaPreData\n\ndef trainModel(trainData, trainLabel):\n    print('Train SVM...')\n    svmClf = SVC(C=4, kernel='rbf')\n    svmClf.fit(trainData, trainLabel)  # 训练SVM\n    return svmClf\n```\n> 训练模型: \n\n        根据加载分析数据后结果，我们借用网格搜索的思想，把要保留的特征在一定范围内选取最优，找出最高准确率，并且把最高准确率的模型存储起来\n```python\n#训练过程\ndef trainDRSVM():\n    startTime = time.time()\n\n    # 加载数据\n    trainData, trainLabel, preData = opencsv()\n    # 模型训练 (数据预处理-降维)\n    optimalSVMClf, pcaPreData = getOptimalAccuracy(trainData, trainLabel, preData)\n\n    storeModel(optimalSVMClf, 'datasets/getting-started/digit-recognizer/ouput/Result_sklearn_SVM.model')\n    storeModel(pcaPreData, 'datasets/getting-started/digit-recognizer/ouput/Result_sklearn_SVM.pcaPreData')\n\n    print(\"finish!\")\n    stopTime = time.time()\n    print('TrainModel store time used:%f s' % (stopTime - startTime))\n\n# 训练模型\ndef trainModel(trainData, trainLabel):\n    print('Train SVM...')\n    svmClf = SVC(C=4, kernel='rbf')\n    svmClf.fit(trainData, trainLabel)  # 训练SVM\n    return svmClf\n\n# 存储模型\ndef storeModel(model, filename):\n    import pickle\n    with open(filename, 'wb') as fw:\n        pickle.dump(model, fw)\n\n# 找出最高准确率\ndef getOptimalAccuracy(trainData, trainLabel, preData):\n    # 分析数据 100个特征左右\n    # analyse_data(trainData)\n    x_train, x_test, y_train, y_test = train_test_split(trainData, trainLabel, test_size=0.1)\n    lineLen, featureLen = np.shape(x_test)\n    # print(lineLen, type(lineLen), featureLen, type(featureLen))\n\n    minErr = 1\n    minSumErr = 0\n    optimalNum = 1\n    optimalLabel = []\n    optimalSVMClf = None\n    pcaPreDataResult = None\n    for i in range(30, 45, 1):\n        # 评估训练结果\n        pcaTrainData,  pcaTestData, pcaPreData = dRCsv(x_train, x_test, preData, i)\n        svmClf = trainModel(pcaTrainData, y_train)\n        svmtestLabel = svmClf.predict(pcaTestData)\n\n        errArr = np.mat(np.ones((lineLen, 1)))\n        sumErrArr = errArr[svmtestLabel != y_test].sum()\n        sumErr = sumErrArr/lineLen\n\n        print('i=%s' % i, lineLen, sumErrArr, sumErr)\n        if sumErr <= minErr:\n            minErr = sumErr\n            minSumErr = sumErrArr\n            optimalNum = i\n            optimalSVMClf = svmClf\n            optimalLabel = svmtestLabel\n            pcaPreDataResult = pcaPreData\n            print(\"i=%s >>>>> \\t\" % i, lineLen, int(minSumErr), 1-minErr)\n    '''\n    展现 准确率与召回率\n        precision 准确率\n        recall 召回率\n        f1-score  准确率和召回率的一个综合得分\n        support 参与比较的数量\n    参考链接：http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report\n    '''\n    # target_names 以 y的label分类为准\n    # target_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n    target_names = [str(i) for i in list(set(y_test))]\n    print(target_names)\n    print(classification_report(y_test, optimalLabel, target_names=target_names))\n    print(\"特征数量= %s, 存在最优解：>>> \\t\" % optimalNum, lineLen, int(minSumErr), 1-minErr)\n    return optimalSVMClf, pcaPreDataResult\n```\n![最优特征和roc分析](/img/competitions/getting-started/digit-recognizer/svm/最优特征数目和roc分析.jpg)\n\n > 测试算法：便携一个函数来测试不同的和函数并计算错误率\n\n    加载训练好存储的模型，对测试集进行分类并存储结果\n```python\ndef preDRSVM():\n    startTime = time.time()\n    # 加载模型和数据\n    optimalSVMClf = getModel('datasets/getting-started/digit-recognizer/ouput/Result_sklearn_SVM.model')\n    pcaPreData = getModel('datasets/getting-started/digit-recognizer/ouput/Result_sklearn_SVM.pcaPreData')\n    \n    # 结果预测\n    testLabel = optimalSVMClf.predict(pcaPreData)\n    #print(\"testLabel = %f\" % testscore)\n    # 结果的输出\n    saveResult(testLabel, 'datasets/getting-started/digit-recognizer/ouput/Result_sklearn_SVM.csv')\n    print(\"finish!\")\n    stopTime = time.time()\n    print('PreModel load time used:%f s' % (stopTime - startTime))\n```\n\n > 使用算法：\n\n    根据之前的分析过程，用一个简短的可直接执行的程序总结整体的处理过程，包括加载数据，数据降维，模型训练，数据测试，输出结果\n```python\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport csv\nimport time\nimport pandas as pd\nimport numpy as np\nfrom numpy import *\nfrom sklearn.decomposition import PCA\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import train_test_split\n\n\n# 加载数据\ndef opencsv():\n    print('Load Data...')\n    # 使用 pandas 打开\n    dataTrain = pd.read_csv(r'datasets/getting-started/digit-recognizer/input/train.csv')\n    dataTest = pd.read_csv(r'datasets/getting-started/digit-recognizer/input/test.csv')\n\n    trainData = dataTrain.values[:, 1:]  # 读入全部训练数据\n    trainLabel = dataTrain.values[:, 0]\n    preData = dataTest.values[:, :]  # 测试全部测试个数据\n    return trainData, trainLabel,preData\n\ndef dRCsv(x_train, x_test, preData, COMPONENT_NUM):\n    print('dimensionality reduction...')\n    trainData = np.array(x_train)\n    testData = np.array(x_test)\n    preData = np.array(preData)\n    pca = PCA(n_components=COMPONENT_NUM, whiten=True)\n    pca.fit(trainData)  # Fit the model with X\n    pcaTrainData = pca.transform(trainData)  # Fit the model with X and 在X上完成降维.\n    pcaTestData = pca.transform(testData)  # Fit the model with X and 在X上完成降维.\n    pcaPreData = pca.transform(preData)  # Fit the model with X and 在X上完成降维.\n    print(sum(pca.explained_variance_ratio_))\n    return pcaTrainData,  pcaTestData, pcaPreData\n\ndef svmClassify(trainData, trainLabel):\n     print('Train SVM...')\n     svmClf=SVC(C=4, kernel='rbf')\n     svmClf.fit(trainData, trainLabel)  # 训练SVM\n     return svmClf\n\ndef saveResult(result, csvName):\n     with open(csvName, 'wb') as myFile:\n         myWriter = csv.writer(myFile)\n         myWriter.writerow([\"ImageId\", \"Label\"])\n         index = 0\n         for i in result:\n            tmp = []\n            index = index+1\n            tmp.append(index)\n            # tmp.append(i)\n            tmp.append(int(i))\n            myWriter.writerow(tmp)\n\ndef SVM():\n     #加载数据\n     start_time = time.time()\n     trainData, trainLable,preData=opencsv()\n     print(\"load data finish\")\n     stop_time_l = time.time()\n     print('load data time used:%f' % (stop_time_l - start_time))\n     trainData, testData,trainLable,testLabletrue = train_test_split(trainData, trainLable, test_size=0.1, random_state=41)#交叉验证 测试集10%\n    \n     #pca降维\n     trainData,testData,preData =dRCsv(trainData,testData,preData,35)  \n     # print (trainData,trainLable)\n\n\n     # 模型训练\n     svmClf=svmClassify(trainData, trainLable)\n     print ('trainsvm finished')\n\n     # 结果预测\n     testLable=svmClf.predict(testData)\n     preLable=svmClf.predict(preData)\n\n     #交叉验证\n     zeroLable=testLabletrue-testLable\n     rightCount=0\n     for i in range(len(zeroLable)):\n       if zeroLable[i]==0:\n          rightCount+=1\n     print ('the right rate is:',float(rightCount)/len(zeroLable))\n     # 结果的输出\n     saveResult(preLable, r'datasets/getting-started/digit-recognizer/ouput/Result_sklearn_SVM.csv')\n     print( \"finish!\")\n     stop_time_r = time.time()\n     print('classify time used:%f' % (stop_time_r - start_time))\n   \nif __name__ == '__main__':\n     SVM()\n\n```\n![svm-simple](/img/competitions/getting-started/digit-recognizer/svm/svm-simple.jpg)\n\n参考文献：\n\n[1] http://bytesizebio.net/2014/02/05/support-vector-machines-explained-well/\n\n[2] http://www.cnblogs.com/en-heng/p/5965438.html\n\n[3] http://blog.csdn.net/on2way/article/details/47729419\n\n[4] http://blog.csdn.net/v_july_v/article/details/7624837\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/digit-recognizer/神经网络算法描述.md",
    "content": "# 树模型\n\n> 神经网络\n\n本节选用sklearn的神经网络模型进行预测,神经网络是cnn的基础，也是机器学习最重要的组成部分，想要更上一层楼的同学需要把神经网络搞明白，具体可参考周志华的《机器学习》，还有花书 如果不想翻书的话，关于神经网络入门的博客一捏一大把，我就放一个连接吧，主要靠大家自己~ \n\nhttps://blog.csdn.net/a819825294/article/details/53393837\n\n> 代码\n\n```python\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.decomposition import PCA\nimport pandas as pd\nimport numpy as np\n\ntrain_data=pd.read_csv(r\"C:\\Users\\312\\Desktop\\digit-recognizer\\train.csv\")\ntest_data=pd.read_csv(r\"C:\\Users\\312\\Desktop\\digit-recognizer\\test.csv\")\ndata=pd.concat([train_data,test_data],axis=0).reset_index(drop=True)\ndata.drop(['label'],axis=1,inplace=True)\nlabel=train_data.label\n\n\npca=PCA(n_components=100, random_state=34)\ndata_pca=pca.fit_transform(data)\n\nXtrain,Ytrain,xtest,ytest=train_test_split(data_pca[0:len(train_data)],label,test_size=0.1, random_state=34)\n\nclf=MLPClassifier(hidden_layer_sizes=(100, ), activation='relu', alpha=0.0001,learning_rate='constant', learning_rate_init=0.001,max_iter=200, shuffle=True, random_state=34)\n\n\nclf.fit(Xtrain,xtest)\ny_predict=clf.predict(Ytrain)\n\n\nzeroLable=ytest-y_predict\nrightCount=0\nfor i in range(len(zeroLable)):\n    if list(zeroLable)[i]==0:\n        rightCount+=1\nprint ('the right rate is:',float(rightCount)/len(zeroLable))\n\n\nresult=clf.predict(data_pca[len(train_data):])\n\n       \nwith open(\"C:\\\\Users\\\\312\\\\Desktop\\\\digit-recognizer\\\\result.csv\", 'w') as fw:\n    with open('C:\\\\Users\\\\312\\\\Desktop\\\\digit-recognizer\\\\sample_submission.csv') as pred_file:\n        fw.write('{},{}\\n'.format('ImageId', 'Label'))\n        for i,line in enumerate(pred_file.readlines()[1:]):\n            splits = line.strip().split(',')\n            fw.write('{},{}\\n'.format(splits[0],result[i]))\n```\n\n> 结果:\n\n```\nthe right rate is: 0.9533333333333334\n```\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/digit-recognizer/随机森林算法描述.md",
    "content": "# 树模型\n\n本节选用随机森林树模型进行演示，也可尝试sklearn和GBDT模型，以及xgboost模型,lightgbm模型 树模型的基础知识请参照周志华的《机器学习》以及李航的《统计学习方法》，另外代码可以参照机器学习实战 树模型最核心的就是信息熵，基尼指数等叶子结点得分，这是树模型分裂的依据，请着重研究~ 想偷懒的同学，社区的连接在此，看看吧\n\n[7.集成方法-随机森林和AdaBoost](https://github.com/apachecn/AiLearning/blob/master/docs/ml/7.%E9%9B%86%E6%88%90%E6%96%B9%E6%B3%95-%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97%E5%92%8CAdaBoost.md)\n\n顺便说一下，嫌慢的同学还是用lightgbm吧，这个超快的~ GridSearchCV是用来调参的，只需要把注释去掉就可以显示出最佳的参数，不过实测慢的要死。。\n\n\n> 代码：\n\n```python\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.decomposition import PCA\nimport pandas as pd\nimport numpy as np\n# from sklearn.grid_search import GridSearchCV\n# from numpy import arange\n# from lightgbm import LGBMClassifier\n\ntrain_data=pd.read_csv(r\"C:\\Users\\312\\Desktop\\digit-recognizer\\train.csv\")\ntest_data=pd.read_csv(r\"C:\\Users\\312\\Desktop\\digit-recognizer\\test.csv\")\ndata=pd.concat([train_data,test_data],axis=0).reset_index(drop=True)\ndata.drop(['label'],axis=1,inplace=True)\nlabel=train_data.label\n\n\npca=PCA(n_components=100, random_state=34)\ndata_pca=pca.fit_transform(data)\n\nXtrain,Ytrain,xtest,ytest=train_test_split(data_pca[0:len(train_data)],label,test_size=0.1, random_state=34)\n\n\nclf=RandomForestClassifier(n_estimators=110,max_depth=5,min_samples_split=2, min_samples_leaf=1,random_state=34)\n\n# clf=LGBMClassifier(num_leaves=63, max_depth=7, n_estimators=80, n_jobs=20)\n\n# param_test1 = {'n_estimators':arange(10,150,10),'max_depth':arange(1,11,1)}\n# gsearch1 = GridSearchCV(estimator = clf, param_grid = param_test1, scoring='accuracy',iid=False,cv=5)\n# gsearch1.fit(Xtrain,xtest)\n# print(gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_)\n\n\nclf.fit(Xtrain,xtest)\ny_predict=clf.predict(Ytrain)\n\n\nzeroLable=ytest-y_predict\nrightCount=0\nfor i in range(len(zeroLable)):\n    if list(zeroLable)[i]==0:\n        rightCount+=1\nprint ('the right rate is:',float(rightCount)/len(zeroLable))\n\n\nresult=clf.predict(data_pca[len(train_data):])\n\nwith open(\"C:\\\\Users\\\\312\\\\Desktop\\\\digit-recognizer\\\\result.csv\", 'w') as fw:\n    with open('C:\\\\Users\\\\312\\\\Desktop\\\\digit-recognizer\\\\sample_submission.csv') as pred_file:\n        fw.write('{},{}\\n'.format('ImageId', 'Label'))\n        for i,line in enumerate(pred_file.readlines()[1:]):\n            splits = line.strip().split(',')\n            fw.write('{},{}\\n'.format(splits[0],result[i]))\n```\n\n> 结果:\n\n```\nthe right rate is: 0.819047619047619\n```\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/house-price/README.md",
    "content": "# **房价预测**\n\n![](/img/competitions/getting-started/house-price/housesbanner.png)\n\n## 比赛说明\n\n* [**房价预测**](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) \n* 要求购房者描述他们的梦想之家，他们可能不会从地下室天花板的高度或与东西方铁路的接近度开始。但是这个游乐场比赛的数据集证明，对价格谈判的影响远远超过卧室或白色栅栏的数量。\n* 有79个解释变量描述（几乎）爱荷华州埃姆斯的住宅房屋的每个方面，这个竞赛挑战你预测每个房屋的最终价格。\n\n## 参赛成员\n\n* 开源组织: [ApacheCN ~ apachecn.org](http://www.apachecn.org/)\n\n## 比赛分析\n\n* 回归问题：价格的问题\n* 常用算法： `回归`、`树回归`、`GBDT`、`xgboost`、`lightGBM`\n\n```\n步骤:\n一. 数据分析\n1. 下载并加载数据\n2. 总体预览:了解每列数据的含义,数据的格式等\n3. 数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备\n\n二. 特征工程\n1.根据业务,常识,以及第二步的数据分析构造特征工程.\n2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)\n\n三. 模型选择\n1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.\n2.比较各个模型的分数,然后取效果较好的模型作为基础模型.\n\n四. 模型融合\n1. 可以参考泰坦尼克号的简单模型融合方式，通过对模型的对比打分方式选择合适的模型\n2. 在房价预测里我们使用模型融合的方法来输出结果，最终的效果很好。\n\n五. 修改特征和模型参数\n1.可以通过添加或者修改特征,提高模型的上限.\n2.通过修改模型的参数,是模型逼近上限\n```\n\n## 一. 数据分析\n\n### 数据下载和加载\n\n* 数据集下载地址：<https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data>\n\n```python\n# 导入相关数据包\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nfrom scipy import stats\nfrom scipy.stats import norm\n```\n\n\n```python\nroot_path = '/opt/data/kaggle/getting-started/house-prices'\n\ntrain = pd.read_csv('%s/%s' % (root_path, 'train.csv'))\ntest = pd.read_csv('%s/%s' % (root_path, 'test.csv'))\n```\n\n### 特征说明\n\n\n```python\ntrain.columns\n```\n\n    Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n           'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n           'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n           'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n           'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n           'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n           'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n           'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n           'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n           'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n           'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n           'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n           'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n           'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n           'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n           'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n           'SaleCondition', 'SalePrice'],\n          dtype='object')\n\n\n![](/img/competitions/getting-started/house-price/房价预测-字段说明.png)\n\n\n```python\ntrain.info()\n```\n\n    <class 'pandas.core.frame.DataFrame'>\n    RangeIndex: 1460 entries, 0 to 1459\n    Data columns (total 81 columns):\n    Id               1460 non-null int64\n    MSSubClass       1460 non-null int64\n    MSZoning         1460 non-null object\n    LotFrontage      1201 non-null float64\n    LotArea          1460 non-null int64\n    Street           1460 non-null object\n    Alley            91 non-null object\n    LotShape         1460 non-null object\n    LandContour      1460 non-null object\n    Utilities        1460 non-null object\n    LotConfig        1460 non-null object\n    LandSlope        1460 non-null object\n    Neighborhood     1460 non-null object\n    Condition1       1460 non-null object\n    Condition2       1460 non-null object\n    BldgType         1460 non-null object\n    HouseStyle       1460 non-null object\n    OverallQual      1460 non-null int64\n    OverallCond      1460 non-null int64\n    YearBuilt        1460 non-null int64\n    YearRemodAdd     1460 non-null int64\n    RoofStyle        1460 non-null object\n    RoofMatl         1460 non-null object\n    Exterior1st      1460 non-null object\n    Exterior2nd      1460 non-null object\n    MasVnrType       1452 non-null object\n    MasVnrArea       1452 non-null float64\n    ExterQual        1460 non-null object\n    ExterCond        1460 non-null object\n    Foundation       1460 non-null object\n    BsmtQual         1423 non-null object\n    BsmtCond         1423 non-null object\n    BsmtExposure     1422 non-null object\n    BsmtFinType1     1423 non-null object\n    BsmtFinSF1       1460 non-null int64\n    BsmtFinType2     1422 non-null object\n    BsmtFinSF2       1460 non-null int64\n    BsmtUnfSF        1460 non-null int64\n    TotalBsmtSF      1460 non-null int64\n    Heating          1460 non-null object\n    HeatingQC        1460 non-null object\n    CentralAir       1460 non-null object\n    Electrical       1459 non-null object\n    1stFlrSF         1460 non-null int64\n    2ndFlrSF         1460 non-null int64\n    LowQualFinSF     1460 non-null int64\n    GrLivArea        1460 non-null int64\n    BsmtFullBath     1460 non-null int64\n    BsmtHalfBath     1460 non-null int64\n    FullBath         1460 non-null int64\n    HalfBath         1460 non-null int64\n    BedroomAbvGr     1460 non-null int64\n    KitchenAbvGr     1460 non-null int64\n    KitchenQual      1460 non-null object\n    TotRmsAbvGrd     1460 non-null int64\n    Functional       1460 non-null object\n    Fireplaces       1460 non-null int64\n    FireplaceQu      770 non-null object\n    GarageType       1379 non-null object\n    GarageYrBlt      1379 non-null float64\n    GarageFinish     1379 non-null object\n    GarageCars       1460 non-null int64\n    GarageArea       1460 non-null int64\n    GarageQual       1379 non-null object\n    GarageCond       1379 non-null object\n    PavedDrive       1460 non-null object\n    WoodDeckSF       1460 non-null int64\n    OpenPorchSF      1460 non-null int64\n    EnclosedPorch    1460 non-null int64\n    3SsnPorch        1460 non-null int64\n    ScreenPorch      1460 non-null int64\n    PoolArea         1460 non-null int64\n    PoolQC           7 non-null object\n    Fence            281 non-null object\n    MiscFeature      54 non-null object\n    MiscVal          1460 non-null int64\n    MoSold           1460 non-null int64\n    YrSold           1460 non-null int64\n    SaleType         1460 non-null object\n    SaleCondition    1460 non-null object\n    SalePrice        1460 non-null int64\n    dtypes: float64(3), int64(35), object(43)\n    memory usage: 924.0+ KB\n\n\n### 特征详情\n\n\n```python\ntrain.head(5)\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>MSSubClass</th>\n      <th>MSZoning</th>\n      <th>LotFrontage</th>\n      <th>LotArea</th>\n      <th>Street</th>\n      <th>Alley</th>\n      <th>LotShape</th>\n      <th>LandContour</th>\n      <th>Utilities</th>\n      <th>...</th>\n      <th>PoolArea</th>\n      <th>PoolQC</th>\n      <th>Fence</th>\n      <th>MiscFeature</th>\n      <th>MiscVal</th>\n      <th>MoSold</th>\n      <th>YrSold</th>\n      <th>SaleType</th>\n      <th>SaleCondition</th>\n      <th>SalePrice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>65.0</td>\n      <td>8450</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>208500</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>20</td>\n      <td>RL</td>\n      <td>80.0</td>\n      <td>9600</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>5</td>\n      <td>2007</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>181500</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>68.0</td>\n      <td>11250</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>223500</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>70</td>\n      <td>RL</td>\n      <td>60.0</td>\n      <td>9550</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2006</td>\n      <td>WD</td>\n      <td>Abnorml</td>\n      <td>140000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>84.0</td>\n      <td>14260</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>12</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>250000</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 81 columns</p>\n</div>\n\n\n\n### 特征分析（统计学与绘图）\n\n每一行是一条房子出售的记录，原始特征有80列，具体的意思可以根据data_description来查询，我们要预测的是房子的售价，即“SalePrice”。训练集有1459条记录，测试集有1460条记录，数据量还是很小的。\n\n\n```python\n# 相关性协方差表,corr()函数,返回结果接近0说明无相关性,大于0说明是正相关,小于0是负相关.\ntrain_corr = train.drop('Id',axis=1).corr()\ntrain_corr\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MSSubClass</th>\n      <th>LotFrontage</th>\n      <th>LotArea</th>\n      <th>OverallQual</th>\n      <th>OverallCond</th>\n      <th>YearBuilt</th>\n      <th>YearRemodAdd</th>\n      <th>MasVnrArea</th>\n      <th>BsmtFinSF1</th>\n      <th>BsmtFinSF2</th>\n      <th>...</th>\n      <th>WoodDeckSF</th>\n      <th>OpenPorchSF</th>\n      <th>EnclosedPorch</th>\n      <th>3SsnPorch</th>\n      <th>ScreenPorch</th>\n      <th>PoolArea</th>\n      <th>MiscVal</th>\n      <th>MoSold</th>\n      <th>YrSold</th>\n      <th>SalePrice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MSSubClass</th>\n      <td>1.000000</td>\n      <td>-0.386347</td>\n      <td>-0.139781</td>\n      <td>0.032628</td>\n      <td>-0.059316</td>\n      <td>0.027850</td>\n      <td>0.040581</td>\n      <td>0.022936</td>\n      <td>-0.069836</td>\n      <td>-0.065649</td>\n      <td>...</td>\n      <td>-0.012579</td>\n      <td>-0.006100</td>\n      <td>-0.012037</td>\n      <td>-0.043825</td>\n      <td>-0.026030</td>\n      <td>0.008283</td>\n      <td>-0.007683</td>\n      <td>-0.013585</td>\n      <td>-0.021407</td>\n      <td>-0.084284</td>\n    </tr>\n    <tr>\n      <th>LotFrontage</th>\n      <td>-0.386347</td>\n      <td>1.000000</td>\n      <td>0.426095</td>\n      <td>0.251646</td>\n      <td>-0.059213</td>\n      <td>0.123349</td>\n      <td>0.088866</td>\n      <td>0.193458</td>\n      <td>0.233633</td>\n      <td>0.049900</td>\n      <td>...</td>\n      <td>0.088521</td>\n      <td>0.151972</td>\n      <td>0.010700</td>\n      <td>0.070029</td>\n      <td>0.041383</td>\n      <td>0.206167</td>\n      <td>0.003368</td>\n      <td>0.011200</td>\n      <td>0.007450</td>\n      <td>0.351799</td>\n    </tr>\n    <tr>\n      <th>LotArea</th>\n      <td>-0.139781</td>\n      <td>0.426095</td>\n      <td>1.000000</td>\n      <td>0.105806</td>\n      <td>-0.005636</td>\n      <td>0.014228</td>\n      <td>0.013788</td>\n      <td>0.104160</td>\n      <td>0.214103</td>\n      <td>0.111170</td>\n      <td>...</td>\n      <td>0.171698</td>\n      <td>0.084774</td>\n      <td>-0.018340</td>\n      <td>0.020423</td>\n      <td>0.043160</td>\n      <td>0.077672</td>\n      <td>0.038068</td>\n      <td>0.001205</td>\n      <td>-0.014261</td>\n      <td>0.263843</td>\n    </tr>\n    <tr>\n      <th>OverallQual</th>\n      <td>0.032628</td>\n      <td>0.251646</td>\n      <td>0.105806</td>\n      <td>1.000000</td>\n      <td>-0.091932</td>\n      <td>0.572323</td>\n      <td>0.550684</td>\n      <td>0.411876</td>\n      <td>0.239666</td>\n      <td>-0.059119</td>\n      <td>...</td>\n      <td>0.238923</td>\n      <td>0.308819</td>\n      <td>-0.113937</td>\n      <td>0.030371</td>\n      <td>0.064886</td>\n      <td>0.065166</td>\n      <td>-0.031406</td>\n      <td>0.070815</td>\n      <td>-0.027347</td>\n      <td>0.790982</td>\n    </tr>\n    <tr>\n      <th>OverallCond</th>\n      <td>-0.059316</td>\n      <td>-0.059213</td>\n      <td>-0.005636</td>\n      <td>-0.091932</td>\n      <td>1.000000</td>\n      <td>-0.375983</td>\n      <td>0.073741</td>\n      <td>-0.128101</td>\n      <td>-0.046231</td>\n      <td>0.040229</td>\n      <td>...</td>\n      <td>-0.003334</td>\n      <td>-0.032589</td>\n      <td>0.070356</td>\n      <td>0.025504</td>\n      <td>0.054811</td>\n      <td>-0.001985</td>\n      <td>0.068777</td>\n      <td>-0.003511</td>\n      <td>0.043950</td>\n      <td>-0.077856</td>\n    </tr>\n    <tr>\n      <th>YearBuilt</th>\n      <td>0.027850</td>\n      <td>0.123349</td>\n      <td>0.014228</td>\n      <td>0.572323</td>\n      <td>-0.375983</td>\n      <td>1.000000</td>\n      <td>0.592855</td>\n      <td>0.315707</td>\n      <td>0.249503</td>\n      <td>-0.049107</td>\n      <td>...</td>\n      <td>0.224880</td>\n      <td>0.188686</td>\n      <td>-0.387268</td>\n      <td>0.031355</td>\n      <td>-0.050364</td>\n      <td>0.004950</td>\n      <td>-0.034383</td>\n      <td>0.012398</td>\n      <td>-0.013618</td>\n      <td>0.522897</td>\n    </tr>\n    <tr>\n      <th>YearRemodAdd</th>\n      <td>0.040581</td>\n      <td>0.088866</td>\n      <td>0.013788</td>\n      <td>0.550684</td>\n      <td>0.073741</td>\n      <td>0.592855</td>\n      <td>1.000000</td>\n      <td>0.179618</td>\n      <td>0.128451</td>\n      <td>-0.067759</td>\n      <td>...</td>\n      <td>0.205726</td>\n      <td>0.226298</td>\n      <td>-0.193919</td>\n      <td>0.045286</td>\n      <td>-0.038740</td>\n      <td>0.005829</td>\n      <td>-0.010286</td>\n      <td>0.021490</td>\n      <td>0.035743</td>\n      <td>0.507101</td>\n    </tr>\n    <tr>\n      <th>MasVnrArea</th>\n      <td>0.022936</td>\n      <td>0.193458</td>\n      <td>0.104160</td>\n      <td>0.411876</td>\n      <td>-0.128101</td>\n      <td>0.315707</td>\n      <td>0.179618</td>\n      <td>1.000000</td>\n      <td>0.264736</td>\n      <td>-0.072319</td>\n      <td>...</td>\n      <td>0.159718</td>\n      <td>0.125703</td>\n      <td>-0.110204</td>\n      <td>0.018796</td>\n      <td>0.061466</td>\n      <td>0.011723</td>\n      <td>-0.029815</td>\n      <td>-0.005965</td>\n      <td>-0.008201</td>\n      <td>0.477493</td>\n    </tr>\n    <tr>\n      <th>BsmtFinSF1</th>\n      <td>-0.069836</td>\n      <td>0.233633</td>\n      <td>0.214103</td>\n      <td>0.239666</td>\n      <td>-0.046231</td>\n      <td>0.249503</td>\n      <td>0.128451</td>\n      <td>0.264736</td>\n      <td>1.000000</td>\n      <td>-0.050117</td>\n      <td>...</td>\n      <td>0.204306</td>\n      <td>0.111761</td>\n      <td>-0.102303</td>\n      <td>0.026451</td>\n      <td>0.062021</td>\n      <td>0.140491</td>\n      <td>0.003571</td>\n      <td>-0.015727</td>\n      <td>0.014359</td>\n      <td>0.386420</td>\n    </tr>\n    <tr>\n      <th>BsmtFinSF2</th>\n      <td>-0.065649</td>\n      <td>0.049900</td>\n      <td>0.111170</td>\n      <td>-0.059119</td>\n      <td>0.040229</td>\n      <td>-0.049107</td>\n      <td>-0.067759</td>\n      <td>-0.072319</td>\n      <td>-0.050117</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>0.067898</td>\n      <td>0.003093</td>\n      <td>0.036543</td>\n      <td>-0.029993</td>\n      <td>0.088871</td>\n      <td>0.041709</td>\n      <td>0.004940</td>\n      <td>-0.015211</td>\n      <td>0.031706</td>\n      <td>-0.011378</td>\n    </tr>\n    <tr>\n      <th>BsmtUnfSF</th>\n      <td>-0.140759</td>\n      <td>0.132644</td>\n      <td>-0.002618</td>\n      <td>0.308159</td>\n      <td>-0.136841</td>\n      <td>0.149040</td>\n      <td>0.181133</td>\n      <td>0.114442</td>\n      <td>-0.495251</td>\n      <td>-0.209294</td>\n      <td>...</td>\n      <td>-0.005316</td>\n      <td>0.129005</td>\n      <td>-0.002538</td>\n      <td>0.020764</td>\n      <td>-0.012579</td>\n      <td>-0.035092</td>\n      <td>-0.023837</td>\n      <td>0.034888</td>\n      <td>-0.041258</td>\n      <td>0.214479</td>\n    </tr>\n    <tr>\n      <th>TotalBsmtSF</th>\n      <td>-0.238518</td>\n      <td>0.392075</td>\n      <td>0.260833</td>\n      <td>0.537808</td>\n      <td>-0.171098</td>\n      <td>0.391452</td>\n      <td>0.291066</td>\n      <td>0.363936</td>\n      <td>0.522396</td>\n      <td>0.104810</td>\n      <td>...</td>\n      <td>0.232019</td>\n      <td>0.247264</td>\n      <td>-0.095478</td>\n      <td>0.037384</td>\n      <td>0.084489</td>\n      <td>0.126053</td>\n      <td>-0.018479</td>\n      <td>0.013196</td>\n      <td>-0.014969</td>\n      <td>0.613581</td>\n    </tr>\n    <tr>\n      <th>1stFlrSF</th>\n      <td>-0.251758</td>\n      <td>0.457181</td>\n      <td>0.299475</td>\n      <td>0.476224</td>\n      <td>-0.144203</td>\n      <td>0.281986</td>\n      <td>0.240379</td>\n      <td>0.344501</td>\n      <td>0.445863</td>\n      <td>0.097117</td>\n      <td>...</td>\n      <td>0.235459</td>\n      <td>0.211671</td>\n      <td>-0.065292</td>\n      <td>0.056104</td>\n      <td>0.088758</td>\n      <td>0.131525</td>\n      <td>-0.021096</td>\n      <td>0.031372</td>\n      <td>-0.013604</td>\n      <td>0.605852</td>\n    </tr>\n    <tr>\n      <th>2ndFlrSF</th>\n      <td>0.307886</td>\n      <td>0.080177</td>\n      <td>0.050986</td>\n      <td>0.295493</td>\n      <td>0.028942</td>\n      <td>0.010308</td>\n      <td>0.140024</td>\n      <td>0.174561</td>\n      <td>-0.137079</td>\n      <td>-0.099260</td>\n      <td>...</td>\n      <td>0.092165</td>\n      <td>0.208026</td>\n      <td>0.061989</td>\n      <td>-0.024358</td>\n      <td>0.040606</td>\n      <td>0.081487</td>\n      <td>0.016197</td>\n      <td>0.035164</td>\n      <td>-0.028700</td>\n      <td>0.319334</td>\n    </tr>\n    <tr>\n      <th>LowQualFinSF</th>\n      <td>0.046474</td>\n      <td>0.038469</td>\n      <td>0.004779</td>\n      <td>-0.030429</td>\n      <td>0.025494</td>\n      <td>-0.183784</td>\n      <td>-0.062419</td>\n      <td>-0.069071</td>\n      <td>-0.064503</td>\n      <td>0.014807</td>\n      <td>...</td>\n      <td>-0.025444</td>\n      <td>0.018251</td>\n      <td>0.061081</td>\n      <td>-0.004296</td>\n      <td>0.026799</td>\n      <td>0.062157</td>\n      <td>-0.003793</td>\n      <td>-0.022174</td>\n      <td>-0.028921</td>\n      <td>-0.025606</td>\n    </tr>\n    <tr>\n      <th>GrLivArea</th>\n      <td>0.074853</td>\n      <td>0.402797</td>\n      <td>0.263116</td>\n      <td>0.593007</td>\n      <td>-0.079686</td>\n      <td>0.199010</td>\n      <td>0.287389</td>\n      <td>0.390857</td>\n      <td>0.208171</td>\n      <td>-0.009640</td>\n      <td>...</td>\n      <td>0.247433</td>\n      <td>0.330224</td>\n      <td>0.009113</td>\n      <td>0.020643</td>\n      <td>0.101510</td>\n      <td>0.170205</td>\n      <td>-0.002416</td>\n      <td>0.050240</td>\n      <td>-0.036526</td>\n      <td>0.708624</td>\n    </tr>\n    <tr>\n      <th>BsmtFullBath</th>\n      <td>0.003491</td>\n      <td>0.100949</td>\n      <td>0.158155</td>\n      <td>0.111098</td>\n      <td>-0.054942</td>\n      <td>0.187599</td>\n      <td>0.119470</td>\n      <td>0.085310</td>\n      <td>0.649212</td>\n      <td>0.158678</td>\n      <td>...</td>\n      <td>0.175315</td>\n      <td>0.067341</td>\n      <td>-0.049911</td>\n      <td>-0.000106</td>\n      <td>0.023148</td>\n      <td>0.067616</td>\n      <td>-0.023047</td>\n      <td>-0.025361</td>\n      <td>0.067049</td>\n      <td>0.227122</td>\n    </tr>\n    <tr>\n      <th>BsmtHalfBath</th>\n      <td>-0.002333</td>\n      <td>-0.007234</td>\n      <td>0.048046</td>\n      <td>-0.040150</td>\n      <td>0.117821</td>\n      <td>-0.038162</td>\n      <td>-0.012337</td>\n      <td>0.026673</td>\n      <td>0.067418</td>\n      <td>0.070948</td>\n      <td>...</td>\n      <td>0.040161</td>\n      <td>-0.025324</td>\n      <td>-0.008555</td>\n      <td>0.035114</td>\n      <td>0.032121</td>\n      <td>0.020025</td>\n      <td>-0.007367</td>\n      <td>0.032873</td>\n      <td>-0.046524</td>\n      <td>-0.016844</td>\n    </tr>\n    <tr>\n      <th>FullBath</th>\n      <td>0.131608</td>\n      <td>0.198769</td>\n      <td>0.126031</td>\n      <td>0.550600</td>\n      <td>-0.194149</td>\n      <td>0.468271</td>\n      <td>0.439046</td>\n      <td>0.276833</td>\n      <td>0.058543</td>\n      <td>-0.076444</td>\n      <td>...</td>\n      <td>0.187703</td>\n      <td>0.259977</td>\n      <td>-0.115093</td>\n      <td>0.035353</td>\n      <td>-0.008106</td>\n      <td>0.049604</td>\n      <td>-0.014290</td>\n      <td>0.055872</td>\n      <td>-0.019669</td>\n      <td>0.560664</td>\n    </tr>\n    <tr>\n      <th>HalfBath</th>\n      <td>0.177354</td>\n      <td>0.053532</td>\n      <td>0.014259</td>\n      <td>0.273458</td>\n      <td>-0.060769</td>\n      <td>0.242656</td>\n      <td>0.183331</td>\n      <td>0.201444</td>\n      <td>0.004262</td>\n      <td>-0.032148</td>\n      <td>...</td>\n      <td>0.108080</td>\n      <td>0.199740</td>\n      <td>-0.095317</td>\n      <td>-0.004972</td>\n      <td>0.072426</td>\n      <td>0.022381</td>\n      <td>0.001290</td>\n      <td>-0.009050</td>\n      <td>-0.010269</td>\n      <td>0.284108</td>\n    </tr>\n    <tr>\n      <th>BedroomAbvGr</th>\n      <td>-0.023438</td>\n      <td>0.263170</td>\n      <td>0.119690</td>\n      <td>0.101676</td>\n      <td>0.012980</td>\n      <td>-0.070651</td>\n      <td>-0.040581</td>\n      <td>0.102821</td>\n      <td>-0.107355</td>\n      <td>-0.015728</td>\n      <td>...</td>\n      <td>0.046854</td>\n      <td>0.093810</td>\n      <td>0.041570</td>\n      <td>-0.024478</td>\n      <td>0.044300</td>\n      <td>0.070703</td>\n      <td>0.007767</td>\n      <td>0.046544</td>\n      <td>-0.036014</td>\n      <td>0.168213</td>\n    </tr>\n    <tr>\n      <th>KitchenAbvGr</th>\n      <td>0.281721</td>\n      <td>-0.006069</td>\n      <td>-0.017784</td>\n      <td>-0.183882</td>\n      <td>-0.087001</td>\n      <td>-0.174800</td>\n      <td>-0.149598</td>\n      <td>-0.037610</td>\n      <td>-0.081007</td>\n      <td>-0.040751</td>\n      <td>...</td>\n      <td>-0.090130</td>\n      <td>-0.070091</td>\n      <td>0.037312</td>\n      <td>-0.024600</td>\n      <td>-0.051613</td>\n      <td>-0.014525</td>\n      <td>0.062341</td>\n      <td>0.026589</td>\n      <td>0.031687</td>\n      <td>-0.135907</td>\n    </tr>\n    <tr>\n      <th>TotRmsAbvGrd</th>\n      <td>0.040380</td>\n      <td>0.352096</td>\n      <td>0.190015</td>\n      <td>0.427452</td>\n      <td>-0.057583</td>\n      <td>0.095589</td>\n      <td>0.191740</td>\n      <td>0.280682</td>\n      <td>0.044316</td>\n      <td>-0.035227</td>\n      <td>...</td>\n      <td>0.165984</td>\n      <td>0.234192</td>\n      <td>0.004151</td>\n      <td>-0.006683</td>\n      <td>0.059383</td>\n      <td>0.083757</td>\n      <td>0.024763</td>\n      <td>0.036907</td>\n      <td>-0.034516</td>\n      <td>0.533723</td>\n    </tr>\n    <tr>\n      <th>Fireplaces</th>\n      <td>-0.045569</td>\n      <td>0.266639</td>\n      <td>0.271364</td>\n      <td>0.396765</td>\n      <td>-0.023820</td>\n      <td>0.147716</td>\n      <td>0.112581</td>\n      <td>0.249070</td>\n      <td>0.260011</td>\n      <td>0.046921</td>\n      <td>...</td>\n      <td>0.200019</td>\n      <td>0.169405</td>\n      <td>-0.024822</td>\n      <td>0.011257</td>\n      <td>0.184530</td>\n      <td>0.095074</td>\n      <td>0.001409</td>\n      <td>0.046357</td>\n      <td>-0.024096</td>\n      <td>0.466929</td>\n    </tr>\n    <tr>\n      <th>GarageYrBlt</th>\n      <td>0.085072</td>\n      <td>0.070250</td>\n      <td>-0.024947</td>\n      <td>0.547766</td>\n      <td>-0.324297</td>\n      <td>0.825667</td>\n      <td>0.642277</td>\n      <td>0.252691</td>\n      <td>0.153484</td>\n      <td>-0.088011</td>\n      <td>...</td>\n      <td>0.224577</td>\n      <td>0.228425</td>\n      <td>-0.297003</td>\n      <td>0.023544</td>\n      <td>-0.075418</td>\n      <td>-0.014501</td>\n      <td>-0.032417</td>\n      <td>0.005337</td>\n      <td>-0.001014</td>\n      <td>0.486362</td>\n    </tr>\n    <tr>\n      <th>GarageCars</th>\n      <td>-0.040110</td>\n      <td>0.285691</td>\n      <td>0.154871</td>\n      <td>0.600671</td>\n      <td>-0.185758</td>\n      <td>0.537850</td>\n      <td>0.420622</td>\n      <td>0.364204</td>\n      <td>0.224054</td>\n      <td>-0.038264</td>\n      <td>...</td>\n      <td>0.226342</td>\n      <td>0.213569</td>\n      <td>-0.151434</td>\n      <td>0.035765</td>\n      <td>0.050494</td>\n      <td>0.020934</td>\n      <td>-0.043080</td>\n      <td>0.040522</td>\n      <td>-0.039117</td>\n      <td>0.640409</td>\n    </tr>\n    <tr>\n      <th>GarageArea</th>\n      <td>-0.098672</td>\n      <td>0.344997</td>\n      <td>0.180403</td>\n      <td>0.562022</td>\n      <td>-0.151521</td>\n      <td>0.478954</td>\n      <td>0.371600</td>\n      <td>0.373066</td>\n      <td>0.296970</td>\n      <td>-0.018227</td>\n      <td>...</td>\n      <td>0.224666</td>\n      <td>0.241435</td>\n      <td>-0.121777</td>\n      <td>0.035087</td>\n      <td>0.051412</td>\n      <td>0.061047</td>\n      <td>-0.027400</td>\n      <td>0.027974</td>\n      <td>-0.027378</td>\n      <td>0.623431</td>\n    </tr>\n    <tr>\n      <th>WoodDeckSF</th>\n      <td>-0.012579</td>\n      <td>0.088521</td>\n      <td>0.171698</td>\n      <td>0.238923</td>\n      <td>-0.003334</td>\n      <td>0.224880</td>\n      <td>0.205726</td>\n      <td>0.159718</td>\n      <td>0.204306</td>\n      <td>0.067898</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>0.058661</td>\n      <td>-0.125989</td>\n      <td>-0.032771</td>\n      <td>-0.074181</td>\n      <td>0.073378</td>\n      <td>-0.009551</td>\n      <td>0.021011</td>\n      <td>0.022270</td>\n      <td>0.324413</td>\n    </tr>\n    <tr>\n      <th>OpenPorchSF</th>\n      <td>-0.006100</td>\n      <td>0.151972</td>\n      <td>0.084774</td>\n      <td>0.308819</td>\n      <td>-0.032589</td>\n      <td>0.188686</td>\n      <td>0.226298</td>\n      <td>0.125703</td>\n      <td>0.111761</td>\n      <td>0.003093</td>\n      <td>...</td>\n      <td>0.058661</td>\n      <td>1.000000</td>\n      <td>-0.093079</td>\n      <td>-0.005842</td>\n      <td>0.074304</td>\n      <td>0.060762</td>\n      <td>-0.018584</td>\n      <td>0.071255</td>\n      <td>-0.057619</td>\n      <td>0.315856</td>\n    </tr>\n    <tr>\n      <th>EnclosedPorch</th>\n      <td>-0.012037</td>\n      <td>0.010700</td>\n      <td>-0.018340</td>\n      <td>-0.113937</td>\n      <td>0.070356</td>\n      <td>-0.387268</td>\n      <td>-0.193919</td>\n      <td>-0.110204</td>\n      <td>-0.102303</td>\n      <td>0.036543</td>\n      <td>...</td>\n      <td>-0.125989</td>\n      <td>-0.093079</td>\n      <td>1.000000</td>\n      <td>-0.037305</td>\n      <td>-0.082864</td>\n      <td>0.054203</td>\n      <td>0.018361</td>\n      <td>-0.028887</td>\n      <td>-0.009916</td>\n      <td>-0.128578</td>\n    </tr>\n    <tr>\n      <th>3SsnPorch</th>\n      <td>-0.043825</td>\n      <td>0.070029</td>\n      <td>0.020423</td>\n      <td>0.030371</td>\n      <td>0.025504</td>\n      <td>0.031355</td>\n      <td>0.045286</td>\n      <td>0.018796</td>\n      <td>0.026451</td>\n      <td>-0.029993</td>\n      <td>...</td>\n      <td>-0.032771</td>\n      <td>-0.005842</td>\n      <td>-0.037305</td>\n      <td>1.000000</td>\n      <td>-0.031436</td>\n      <td>-0.007992</td>\n      <td>0.000354</td>\n      <td>0.029474</td>\n      <td>0.018645</td>\n      <td>0.044584</td>\n    </tr>\n    <tr>\n      <th>ScreenPorch</th>\n      <td>-0.026030</td>\n      <td>0.041383</td>\n      <td>0.043160</td>\n      <td>0.064886</td>\n      <td>0.054811</td>\n      <td>-0.050364</td>\n      <td>-0.038740</td>\n      <td>0.061466</td>\n      <td>0.062021</td>\n      <td>0.088871</td>\n      <td>...</td>\n      <td>-0.074181</td>\n      <td>0.074304</td>\n      <td>-0.082864</td>\n      <td>-0.031436</td>\n      <td>1.000000</td>\n      <td>0.051307</td>\n      <td>0.031946</td>\n      <td>0.023217</td>\n      <td>0.010694</td>\n      <td>0.111447</td>\n    </tr>\n    <tr>\n      <th>PoolArea</th>\n      <td>0.008283</td>\n      <td>0.206167</td>\n      <td>0.077672</td>\n      <td>0.065166</td>\n      <td>-0.001985</td>\n      <td>0.004950</td>\n      <td>0.005829</td>\n      <td>0.011723</td>\n      <td>0.140491</td>\n      <td>0.041709</td>\n      <td>...</td>\n      <td>0.073378</td>\n      <td>0.060762</td>\n      <td>0.054203</td>\n      <td>-0.007992</td>\n      <td>0.051307</td>\n      <td>1.000000</td>\n      <td>0.029669</td>\n      <td>-0.033737</td>\n      <td>-0.059689</td>\n      <td>0.092404</td>\n    </tr>\n    <tr>\n      <th>MiscVal</th>\n      <td>-0.007683</td>\n      <td>0.003368</td>\n      <td>0.038068</td>\n      <td>-0.031406</td>\n      <td>0.068777</td>\n      <td>-0.034383</td>\n      <td>-0.010286</td>\n      <td>-0.029815</td>\n      <td>0.003571</td>\n      <td>0.004940</td>\n      <td>...</td>\n      <td>-0.009551</td>\n      <td>-0.018584</td>\n      <td>0.018361</td>\n      <td>0.000354</td>\n      <td>0.031946</td>\n      <td>0.029669</td>\n      <td>1.000000</td>\n      <td>-0.006495</td>\n      <td>0.004906</td>\n      <td>-0.021190</td>\n    </tr>\n    <tr>\n      <th>MoSold</th>\n      <td>-0.013585</td>\n      <td>0.011200</td>\n      <td>0.001205</td>\n      <td>0.070815</td>\n      <td>-0.003511</td>\n      <td>0.012398</td>\n      <td>0.021490</td>\n      <td>-0.005965</td>\n      <td>-0.015727</td>\n      <td>-0.015211</td>\n      <td>...</td>\n      <td>0.021011</td>\n      <td>0.071255</td>\n      <td>-0.028887</td>\n      <td>0.029474</td>\n      <td>0.023217</td>\n      <td>-0.033737</td>\n      <td>-0.006495</td>\n      <td>1.000000</td>\n      <td>-0.145721</td>\n      <td>0.046432</td>\n    </tr>\n    <tr>\n      <th>YrSold</th>\n      <td>-0.021407</td>\n      <td>0.007450</td>\n      <td>-0.014261</td>\n      <td>-0.027347</td>\n      <td>0.043950</td>\n      <td>-0.013618</td>\n      <td>0.035743</td>\n      <td>-0.008201</td>\n      <td>0.014359</td>\n      <td>0.031706</td>\n      <td>...</td>\n      <td>0.022270</td>\n      <td>-0.057619</td>\n      <td>-0.009916</td>\n      <td>0.018645</td>\n      <td>0.010694</td>\n      <td>-0.059689</td>\n      <td>0.004906</td>\n      <td>-0.145721</td>\n      <td>1.000000</td>\n      <td>-0.028923</td>\n    </tr>\n    <tr>\n      <th>SalePrice</th>\n      <td>-0.084284</td>\n      <td>0.351799</td>\n      <td>0.263843</td>\n      <td>0.790982</td>\n      <td>-0.077856</td>\n      <td>0.522897</td>\n      <td>0.507101</td>\n      <td>0.477493</td>\n      <td>0.386420</td>\n      <td>-0.011378</td>\n      <td>...</td>\n      <td>0.324413</td>\n      <td>0.315856</td>\n      <td>-0.128578</td>\n      <td>0.044584</td>\n      <td>0.111447</td>\n      <td>0.092404</td>\n      <td>-0.021190</td>\n      <td>0.046432</td>\n      <td>-0.028923</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>37 rows × 37 columns</p>\n</div>\n\n\n> 所有特征相关度分析\n\n\n```python\n# 画出相关性热力图\na = plt.subplots(figsize=(20, 12))#调整画布大小\na = sns.heatmap(train_corr, vmax=.8, square=True)#画热力图   annot=True 显示系数\n```\n\n\n![png](/img/competitions/getting-started/house-price/output_14_0.png)\n\n\n> SalePrice 相关度特征排序\n\n\n```python\n# 寻找K个最相关的特征信息\nk = 10 # number of variables for heatmap\ncols = train_corr.nlargest(k, 'SalePrice')['SalePrice'].index\ncm = np.corrcoef(train[cols].values.T)\nsns.set(font_scale=1.5)\nhm = plt.subplots(figsize=(20, 12))#调整画布大小\nhm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)\nplt.show()\n\n'''\n1. GarageCars 和 GarageAre 相关性很高、就像双胞胎一样，所以我们只需要其中的一个变量，例如：GarageCars。\n2. TotalBsmtSF  和 1stFloor 与上述情况相同，我们选择 TotalBsmtS\n3. GarageAre 和 TotRmsAbvGrd 与上述情况相同，我们选择 GarageAre\n''' \n```\n\n\n![png](/img/competitions/getting-started/house-price/output_16_0.png)\n\n\n    '\\n1. GarageCars 和 GarageAre 相关性很高、就像双胞胎一样，所以我们只需要其中的一个变量，例如：GarageCars。\\n2. TotalBsmtSF  和 1stFloor 与上述情况相同，我们选择 TotalBsmtS\\n3. GarageAre 和 TotRmsAbvGrd 与上述情况相同，我们选择 GarageAre\\n'\n\n\n\n> SalePrice 和相关变量之间的散点图\n\n\n```python\nsns.set()\ncols = ['SalePrice', 'OverallQual', 'GrLivArea','GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']\nsns.pairplot(train[cols], size = 2.5)\nplt.show();\n```\n\n\n![png](/img/competitions/getting-started/house-price/output_18_0.png)\n\n\n```python\ntrain[['SalePrice', 'OverallQual', 'GrLivArea','GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']].info()\n```\n\n    <class 'pandas.core.frame.DataFrame'>\n    RangeIndex: 1460 entries, 0 to 1459\n    Data columns (total 7 columns):\n    SalePrice      1460 non-null int64\n    OverallQual    1460 non-null int64\n    GrLivArea      1460 non-null int64\n    GarageCars     1460 non-null int64\n    TotalBsmtSF    1460 non-null int64\n    FullBath       1460 non-null int64\n    YearBuilt      1460 non-null int64\n    dtypes: int64(7)\n    memory usage: 79.9 KB\n\n## 二. 特征工程\n\n```\ntest['SalePrice'] = None\ntrain_test = pd.concat((train, test)).reset_index(drop=True)\n```\n\n### 1. 缺失值分析\n\n2. 根据业务,常识,以及第二步的数据分析构造特征工程.\n2. 将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)\n\n\n```python\ntotal= train_test.isnull().sum().sort_values(ascending=False)\npercent = (train_test.isnull().sum()/train_test.isnull().count()).sort_values(ascending=False)\nmissing_data = pd.concat([total, percent], axis=1, keys=['Total','Lost Percent'])\n\nprint(missing_data[missing_data.isnull().values==False].sort_values('Total', axis=0, ascending=False).head(20))\n\n\n'''\n1. 对于缺失率过高的特征，例如 超过15% 我们应该删掉相关变量且假设该变量并不存在\n2. GarageX 变量群的缺失数据量和概率都相同，可以选择一个就行，例如：GarageCars\n3. 对于缺失数据在5%左右（缺失率低），可以直接删除/回归预测\n'''\n```\n\n\n    '\\n1. 对于缺失率过高的特征，例如 超过15% 我们应该删掉相关变量且假设该变量并不存在\\n2. GarageX 变量群的缺失数据量和概率都相同，可以选择一个就行，例如：GarageCars\\n3. 对于缺失数据在5%左右（缺失率低），可以直接删除/回归预测\\n'\n\n\n```python\ntrain_test = train_test.drop((missing_data[missing_data['Total'] > 1]).index.drop('SalePrice') , axis=1)\n# train_test = train_test.drop(train.loc[train['Electrical'].isnull()].index)\n\ntmp = train_test[train_test['SalePrice'].isnull().values==False]\nprint(tmp.isnull().sum().max()) # justchecking that there's no missing data missing\n```\n\n\n    1\n\n\n\n### 2. 异常值处理\n\n#### 单因素分析\n\n这里的关键在于如何建立阈值，定义一个观察值为异常值。我们对数据进行正态化，意味着把数据值转换成均值为 0，方差为 1 的数据\n\n\n```python\nfig = plt.figure(figsize=(12, 6))\nax1 = fig.add_subplot(1, 2, 1)\nax2 = fig.add_subplot(1, 2, 2)\nax1.hist(train.SalePrice)\nax2.hist(np.log1p(train.SalePrice))\n\n'''\n从直方图中可以看出：\n\n* 偏离正态分布\n* 数据正偏\n* 有峰值\n'''\n# 数据偏度和峰度度量：\n\nprint(\"Skewness: %f\" % train['SalePrice'].skew())\nprint(\"Kurtosis: %f\" % train['SalePrice'].kurt())\n\n'''\n低范围的值都比较相似并且在 0 附近分布。\n高范围的值离 0 很远，并且七点几的值远在正常范围之外。\n'''\n```\n\n\n    '\\n低范围的值都比较相似并且在 0 附近分布。\\n高范围的值离 0 很远，并且七点几的值远在正常范围之外。\\n'\n\n\n![png](/img/competitions/getting-started/house-price/output_25_1.png)\n\n\n#### 双变量分析\n\n> 1.GrLivArea 和 SalePrice 双变量分析\n\n\n```python\nvar = 'GrLivArea'\ndata = pd.concat([train['SalePrice'], train[var]], axis=1)\ndata.plot.scatter(x=var, y='SalePrice', ylim=(0,800000));\n\n'''\n从图中可以看出：\n\n1. 有两个离群的 GrLivArea 值很高的数据，我们可以推测出现这种情况的原因。\n    或许他们代表了农业地区，也就解释了低价。 这两个点很明显不能代表典型样例，所以我们将它们定义为异常值并删除。\n2. 图中顶部的两个点是七点几的观测值，他们虽然看起来像特殊情况，但是他们依然符合整体趋势，所以我们将其保留下来。\n'''\n```\n\n\n    '\\n从图中可以看出：\\n\\n1. 有两个离群的 GrLivArea 值很高的数据，我们可以推测出现这种情况的原因。\\n    或许他们代表了农业地区，也就解释了低价。 这两个点很明显不能代表典型样例，所以我们将它们定义为异常值并删除。\\n2. 图中顶部的两个点是七点几的观测值，他们虽然看起来像特殊情况，但是他们依然符合整体趋势，所以我们将其保留下来。\\n'\n\n\n\n\n![png](/img/competitions/getting-started/house-price/output_27_1.png)\n\n\n\n```python\n# 删除点\nprint(train.sort_values(by='GrLivArea', ascending = False)[:2])\ntmp = train_test[train_test['SalePrice'].isnull().values==False]\n\ntrain_test = train_test.drop(tmp[tmp['Id'] == 1299].index)\ntrain_test = train_test.drop(tmp[tmp['Id'] == 524].index)\n```\n\n> 2.TotalBsmtSF 和 SalePrice 双变量分析\n\n\n```python\nvar = 'TotalBsmtSF'\ndata = pd.concat([train['SalePrice'],train[var]], axis=1)\ndata.plot.scatter(x=var, y='SalePrice',ylim=(0,800000))\n```\n\n\n![png](/img/competitions/getting-started/house-price/output_30_0.png)\n\n\n### 核心部分\n\n“房价” 到底是谁？\n\n这个问题的答案，需要我们验证根据数据基础进行多元分析的假设。\n\n我们已经进行了数据清洗，并且发现了 SalePrice 的很多信息，现在我们要更进一步理解 SalePrice 如何遵循统计假设，可以让我们应用多元技术。\n\n应该测量 4 个假设量：\n\n* 正态性\n* 同方差性\n* 线性\n* 相关错误缺失\n\n#### 正态性：\n\n应主要关注以下两点：直方图 – 峰度和偏度。\n\n\n正态概率图 – 数据分布应紧密跟随代表正态分布的对角线。\n\n1.  SalePrice 绘制直方图和正态概率图：\n\n\n```python\nsns.distplot(train['SalePrice'], fit=norm)\nfig = plt.figure()\nres = stats.probplot(train['SalePrice'], plot=plt)\n\n'''\n可以看出，房价分布不是正态的，显示了峰值，正偏度，但是并不跟随对角线。\n可以用对数变换来解决这个问题\n'''\n```\n\n\n    '\\n可以看出，房价分布不是正态的，显示了峰值，正偏度，但是并不跟随对角线。\\n可以用对数变换来解决这个问题\\n'\n\n\n![png](/img/competitions/getting-started/house-price/output_33_1.png)\n\n\n![png](/img/competitions/getting-started/house-price/output_33_2.png)\n\n\n\n```python\n# 进行对数变换：\n# 进行对数变换：\ntrain_test['SalePrice'] = [i if i is None else np.log1p(i) for i in train_test['SalePrice']]\n```\n\n\n```python\n# 绘制变换后的直方图和正态概率图：\ntmp = train_test[train_test['SalePrice'].isnull().values==False]\n\nsns.distplot(tmp[tmp['SalePrice'] !=0]['SalePrice'], fit=norm);\nfig = plt.figure()\nres = stats.probplot(tmp['SalePrice'], plot=plt)\n```\n\n\n![png](/img/competitions/getting-started/house-price/output_35_0.png)\n\n\n![png](/img/competitions/getting-started/house-price/output_35_1.png)\n\n\n#### 2. GrLivArea\n绘制直方图和正态概率曲线图：\n\n\n```python\nsns.distplot(train['GrLivArea'], fit=norm);\nfig = plt.figure()\nres = stats.probplot(train['GrLivArea'], plot=plt)\n```\n\n\n![png](/img/competitions/getting-started/house-price/output_37_0.png)\n\n\n![png](/img/competitions/getting-started/house-price/output_37_1.png)\n\n\n\n```python\n# 进行对数变换：\ntrain_test['GrLivArea'] = [i if i is None else np.log1p(i) for i in train_test['GrLivArea']]\n\n# 绘制变换后的直方图和正态概率图：\ntmp = train_test[train_test['SalePrice'].isnull().values==False]\nsns.distplot(tmp['GrLivArea'], fit=norm)\nfig = plt.figure()\nres = stats.probplot(tmp['GrLivArea'], plot=plt)\n```\n\n\n![png](/img/competitions/getting-started/house-price/output_38_0.png)\n\n\n![png](/img/competitions/getting-started/house-price/output_38_1.png)\n\n\n#### 3.TotalBsmtSF\n\n绘制直方图和正态概率曲线图：\n\n\n```python\nsns.distplot(train['TotalBsmtSF'],fit=norm);\nfig = plt.figure()\nres = stats.probplot(train['TotalBsmtSF'],plot=plt)\n\n'''\n从图中可以看出：\n* 显示出了偏度\n* 大量为 0(Y值) 的观察值（没有地下室的房屋）\n* 含 0(Y值) 的数据无法进行对数变换\n'''\n```\n\n    '\\n从图中可以看出：\\n* 显示出了偏度\\n* 大量为 0(Y值) 的观察值（没有地下室的房屋）\\n* 含 0(Y值) 的数据无法进行对数变换\\n'\n\n\n![png](/img/competitions/getting-started/house-price/output_40_1.png)\n\n\n![png](/img/competitions/getting-started/house-price/output_40_2.png)\n\n\n\n```python\n# 去掉为0的分布情况\ntmp = train_test[train_test['SalePrice'].isnull().values==False]\n\ntmp = np.array(tmp.loc[tmp['TotalBsmtSF']>0, ['TotalBsmtSF']])[:, 0]\nsns.distplot(tmp, fit=norm)\nfig = plt.figure()\nres = stats.probplot(tmp, plot=plt)\n```\n\n\n![png](/img/competitions/getting-started/house-price/output_41_0.png)\n\n\n\n![png](/img/competitions/getting-started/house-price/output_41_1.png)\n\n\n\n```python\n# 我们建立了一个变量，可以得到有没有地下室的影响值（二值变量），我们选择忽略零值，只对非零值进行对数变换。\n# 这样我们既可以变换数据，也不会损失有没有地下室的影响。\n\nprint(train.loc[train['TotalBsmtSF']==0, ['TotalBsmtSF']].count())\ntrain.loc[train['TotalBsmtSF']==0,'TotalBsmtSF'] = 1\nprint(train.loc[train['TotalBsmtSF']==1, ['TotalBsmtSF']].count())\n```\n\n    TotalBsmtSF    37\n    dtype: int64\n    TotalBsmtSF    37\n    dtype: int64\n\n\n\n```python\n# 进行对数变换：\ntmp = train_test[train_test['SalePrice'].isnull().values==False]\n\nprint(tmp['TotalBsmtSF'].head(10))\ntrain_test['TotalBsmtSF']= np.log1p(train_test['TotalBsmtSF'])\n\ntmp = train_test[train_test['SalePrice'].isnull().values==False]\nprint(tmp['TotalBsmtSF'].head(10))\n```\n\n    0     856.0\n    1    1262.0\n    2     920.0\n    3     756.0\n    4    1145.0\n    5     796.0\n    6    1686.0\n    7    1107.0\n    8     952.0\n    9     991.0\n    Name: TotalBsmtSF, dtype: float64\n    0    6.753438\n    1    7.141245\n    2    6.825460\n    3    6.629363\n    4    7.044033\n    5    6.680855\n    6    7.430707\n    7    7.010312\n    8    6.859615\n    9    6.899723\n    Name: TotalBsmtSF, dtype: float64\n\n\n\n```python\n# 绘制变换后的直方图和正态概率图：\ntmp = train_test[train_test['SalePrice'].isnull().values==False]\n\ntmp = np.array(tmp.loc[tmp['TotalBsmtSF']>0, ['TotalBsmtSF']])[:, 0]\nsns.distplot(tmp, fit=norm)\nfig = plt.figure()\nres = stats.probplot(tmp, plot=plt)\n```\n\n\n![png](/img/competitions/getting-started/house-price/output_44_0.png)\n\n\n\n![png](/img/competitions/getting-started/house-price/output_44_1.png)\n\n\n#### 同方差性：\n\n最好的测量两个变量的同方差性的方法就是图像。\n\n1.  SalePrice 和 GrLivArea 同方差性\n\n绘制散点图：\n\n\n\n```python\ntmp = train_test[train_test['SalePrice'].isnull().values==False]\n\nplt.scatter(tmp['GrLivArea'], tmp['SalePrice'])\n```\n\n\n    <matplotlib.collections.PathCollection at 0x11a366f60>\n\n\n![png](/img/competitions/getting-started/house-price/output_46_1.png)\n\n\n2. SalePrice with TotalBsmtSF 同方差性\n\n绘制散点图：\n\n\n```python\ntmp = train_test[train_test['SalePrice'].isnull().values==False]\n\nplt.scatter(tmp[tmp['TotalBsmtSF']>0]['TotalBsmtSF'], tmp[tmp['TotalBsmtSF']>0]['SalePrice'])\n\n# 可以看出 SalePrice 在整个 TotalBsmtSF 变量范围内显示出了同等级别的变化。\n```\n\n\n    <matplotlib.collections.PathCollection at 0x11d7d96d8>\n\n\n\n\n![png](/img/competitions/getting-started/house-price/output_48_1.png)\n\n\n## 三. 模型选择\n\n### 1.数据标准化\n\n\n```python\ntmp = train_test[train_test['SalePrice'].isnull().values==False]\ntmp_1 = train_test[train_test['SalePrice'].isnull().values==True]\n\nx_train = tmp[['OverallQual', 'GrLivArea','GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']]\ny_train = tmp[[\"SalePrice\"]].values.ravel()\nx_test = tmp_1[['OverallQual', 'GrLivArea','GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']]\n\n# 简单测试，用中位数来替代\n# print(x_test.GarageCars.mean(), x_test.GarageCars.median(), x_test.TotalBsmtSF.mean(), x_test.TotalBsmtSF.median())\n\nx_test[\"GarageCars\"].fillna(x_test.GarageCars.median(), inplace=True)\nx_test[\"TotalBsmtSF\"].fillna(x_test.TotalBsmtSF.median(), inplace=True)\n```\n\n### 2.开始建模\n\n1. 可选单个模型模型有 线性回归（Ridge、Lasso）、树回归、GBDT、XGBoost、LightGBM 等.\n2. 也可以将多个模型组合起来,进行模型融合,比如voting,stacking等方法\n3. 好的特征决定模型上限,好的模型和参数可以无线逼近上限.\n4. 我测试了多种模型,模型结果最高的随机森林,最高有0.8.\n\n#### bagging:\n\n单个分类器的效果真的是很有限。\n我们会倾向于把N多的分类器合在一起，做一个“综合分类器”以达到最好的效果。\n我们从刚刚的试验中得知，Ridge(alpha=15)给了我们最好的结果。\n\n\n```python\nfrom sklearn.linear_model import Ridge\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.ensemble import BaggingRegressor, RandomForestRegressor\n\nridge = Ridge(alpha=0.1)\n\n# bagging 把很多小的分类器放在一起，每个train随机的一部分数据，然后把它们的最终结果综合起来（多数投票）\n# bagging 算是一种算法框架\nparams = [1, 10, 20, 40, 60]\ntest_scores = []\nfor param in params:\n    clf = BaggingRegressor(base_estimator=ridge, n_estimators=param)\n    # cv=5表示cross_val_score采用的是k-fold cross validation的方法，重复5次交叉验证\n    # scoring='precision'、scoring='recall'、scoring='f1', scoring='neg_mean_squared_error' 方差值\n    test_score = np.sqrt(-cross_val_score(clf, x_train, y_train, cv=10, scoring='neg_mean_squared_error'))\n    test_scores.append(np.mean(test_score))\n\nprint(test_score.mean())\nplt.plot(params, test_scores)\nplt.title('n_estimators vs CV Error')\nplt.show()\n```\n\n\n![png](/img/competitions/getting-started/house-price/output_53_0.png)\n```python\n# 模型选择\n## LASSO Regression :\nlasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=1))\n* Elastic Net Regression\nENet = make_pipeline(\n    RobustScaler(), ElasticNet(\n        alpha=0.0005, l1_ratio=.9, random_state=3))\nKernel Ridge Regression\nKRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)\n## Gradient Boosting Regression\nGBoost = GradientBoostingRegressor(\n    n_estimators=3000,\n    learning_rate=0.05,\n    max_depth=4,\n    max_features='sqrt',\n    min_samples_leaf=15,\n    min_samples_split=10,\n    loss='huber',\n    random_state=5)\n## XGboost\nmodel_xgb = xgb.XGBRegressor(\n    colsample_bytree=0.4603,\n    gamma=0.0468,\n    learning_rate=0.05,\n    max_depth=3,\n    min_child_weight=1.7817,\n    n_estimators=2200,\n    reg_alpha=0.4640,\n    reg_lambda=0.8571,\n    subsample=0.5213,\n    silent=1,\n    random_state=7,\n    nthread=-1)\n## lightGBM\nmodel_lgb = lgb.LGBMRegressor(\n    objective='regression',\n    num_leaves=5,\n    learning_rate=0.05,\n    n_estimators=720,\n    max_bin=55,\n    bagging_fraction=0.8,\n    bagging_freq=5,\n    feature_fraction=0.2319,\n    feature_fraction_seed=9,\n    bagging_seed=9,\n    min_data_in_leaf=6,\n    min_sum_hessian_in_leaf=11)\n## 对这些基本模型进行打分\nscore = rmsle_cv(lasso)\nprint(\"\\nLasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\nscore = rmsle_cv(ENet)\nprint(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\nscore = rmsle_cv(KRR)\nprint(\n    \"Kernel Ridge score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\nscore = rmsle_cv(GBoost)\nprint(\"Gradient Boosting score: {:.4f} ({:.4f})\\n\".format(score.mean(),\n                                                          score.std()))\nscore = rmsle_cv(model_xgb)\nprint(\"Xgboost score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\nscore = rmsle_cv(model_lgb)\nprint(\"LGBM score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n```\n\n\n```python\nfrom sklearn.linear_model import Ridge\nfrom sklearn.model_selection import learning_curve\n\nridge = Ridge(alpha=0.1)\n\ntrain_sizes, train_loss, test_loss = learning_curve(ridge, x_train, y_train, cv=10, \n                                                    scoring='neg_mean_squared_error',\n                                                    train_sizes = [0.1, 0.3, 0.5, 0.7, 0.9 , 0.95, 1])\n\n# 训练误差均值\ntrain_loss_mean = -np.mean(train_loss, axis = 1)\n# 测试误差均值\ntest_loss_mean = -np.mean(test_loss, axis = 1)\n\n# 绘制误差曲线\nplt.plot(train_sizes/len(x_train), train_loss_mean, 'o-', color = 'r', label = 'Training')\nplt.plot(train_sizes/len(x_train), test_loss_mean, 'o-', color = 'g', label = 'Cross-Validation')\n\nplt.xlabel('Training data size')\nplt.ylabel('Loss')\nplt.legend(loc = 'best')\nplt.show()\n```\n\n\n![png](/img/competitions/getting-started/house-price/output_54_0.png)\n\n\n\n```python\nmode_br = BaggingRegressor(base_estimator=ridge, n_estimators=10)\nmode_br.fit(x_train, y_train)\ny_test = np.expm1(mode_br.predict(x_test))\n```\n\n## 四 建立模型\n\n> 模型融合 voting\n\n```python\n# 模型融合\nclass AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):\n    def __init__(self, models):\n        self.models = models\n\n    # we define clones of the original models to fit the data in\n    def fit(self, X, y):\n        self.models_ = [clone(x) for x in self.models]\n\n        # Train cloned base models\n        for model in self.models_:\n            model.fit(X, y)\n\n        return self\n\n    # Now we do the predictions for cloned models and average them\n    def predict(self, X):\n        predictions = np.column_stack(\n            [model.predict(X) for model in self.models_])\n        return np.mean(predictions, axis=1)\n\n\n# 评价这四个模型的好坏\naveraged_models = AveragingModels(models=(ENet, GBoost, KRR, lasso))\nscore = rmsle_cv(averaged_models)\nprint(\" Averaged base models score: {:.4f} ({:.4f})\\n\".format(score.mean(),\n                                                              score.std()))\n\n# 最终对模型的训练和预测\n# StackedRegressor\nstacked_averaged_models.fit(train.values, y_train)\nstacked_train_pred = stacked_averaged_models.predict(train.values)\nstacked_pred = np.expm1(stacked_averaged_models.predict(test.values))\nprint(rmsle(y_train, stacked_train_pred))\n\n# XGBoost\nmodel_xgb.fit(train, y_train)\nxgb_train_pred = model_xgb.predict(train)\nxgb_pred = np.expm1(model_xgb.predict(test))\nprint(rmsle(y_train, xgb_train_pred))\n# lightGBM\nmodel_lgb.fit(train, y_train)\nlgb_train_pred = model_lgb.predict(train)\nlgb_pred = np.expm1(model_lgb.predict(test.values))\nprint(rmsle(y_train, lgb_train_pred))\n'''RMSE on the entire Train data when averaging'''\n\nprint('RMSLE score on train data:')\nprint(rmsle(y_train, stacked_train_pred * 0.70 + xgb_train_pred * 0.15 +\n            lgb_train_pred * 0.15))\n# 模型融合的预测效果\nensemble = stacked_pred * 0.70 + xgb_pred * 0.15 + lgb_pred * 0.15\n# 保存结果\nresult = pd.DataFrame()\nresult['Id'] = test_ID\nresult['SalePrice'] = ensemble\n# index=False 是用来除去行编号\nresult.to_csv('/Users/liudong/Desktop/house_price/result.csv', index=False)\n```\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/house-price/house-price.md",
    "content": "## **房价预测**\n\n\n[房价预测](https://www.kaggle.com/c/house-prices-advanced-regression-techniques):Predict sales prices and practice feature engineering\n\n### **内容说明**\n\n- Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.\n\n- With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.\n\n### **开发流程**\n\n>收集数据:[数据集](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)\n\n\n>准备数据:\n```python\n#这里对数据做一些转换,原因要么是某些类别个数太少而且分布相近,要么是特征内的值之间有较为明显的优先级\nmapper = {'LandSlope': {'Gtl':'Gtl', 'Mod':'unGtl', 'Sev':'unGtl'},\n          'LotShape': {'Reg':'Reg', 'IR1':'IR1', 'IR2':'other', 'IR3':'other'},\n          'RoofMatl': {'ClyTile':'other', 'CompShg':'CompShg', 'Membran':'other', 'Metal':'other',\n                       'Roll':'other', 'Tar&Grv':'Tar&Grv', 'WdShake':'WdShake', 'WdShngl':'WdShngl'},\n          'Heating':{'GasA':'GasA', 'GasW':'GasW', 'Grav':'Grav', 'Floor':'other',\n                     'OthW':'other', 'Wall':'Wall'},\n          'HeatingQC':{'Po':1, 'Fa':2, 'TA':3, 'Gd':4, 'Ex':5},\n          'KitchenQual': {'Fa':1, 'TA':2, 'Gd':3, 'Ex':4}        \n        }\n\n#对结果影响很小,或者与其他特征相关性较高的特征将被丢弃\nto_drop = ['Id','Street','Utilities','Condition2','PoolArea','PoolQC','Fence',\n           'YrSold','MoSold','BsmtHalfBath','BsmtFinSF2','GarageQual','MiscVal'\n           ,'EnclosedPorch','3SsnPorch','GarageArea','TotRmsAbvGrd','GarageYrBlt'\n           ,'BsmtFinType2','BsmtUnfSF','GarageCond'\n           ,'GarageFinish','FireplaceQu','BsmtCond','BsmtQual','Alley']\n\n\n#特渣工程之瞎搞特征,别问我思路是什么,纯属乱拍脑袋搞出来,而且对结果貌似也仅有一点点影响\n'''\ndata['house_remod']:  重新装修的年份与房建年份的差值\ndata['livingRate']:   LotArea查了下是地块面积,这个特征是居住面积/地块面积*总体评价\ndata['lot_area']:    LotFrontage是与房子相连的街道大小,现在想了下把GrLivArea换成LotArea会不会好点?\ndata['room_area']:   房间数/居住面积\ndata['fu_room']:    带有浴室的房间占总房间数的比例\ndata['gr_room']:    卧室与房间数的占比\n'''\ndef create_feature(data):\n    #是否拥有地下室\n    hBsmt_index = data.index[data['TotalBsmtSF']>0]\n    data['HaveBsmt'] = 0;\n    data.loc[hBsmt_index,'HaveBsmt'] = 1\n    data['house_remod'] = data['YearRemodAdd']-data['YearBuilt'];\n    data['livingRate'] = (data['GrLivArea']/data['LotArea'])*data['OverallCond'];\n    data['lot_area'] = data['LotFrontage']/data['GrLivArea'];\n    data['room_area'] = data['TotRmsAbvGrd']/data['GrLivArea'];\n    data['fu_room'] = data['FullBath']/data['TotRmsAbvGrd'];\n    data['gr_room'] = data['BedroomAbvGr']/data['TotRmsAbvGrd'];\n\ndef processing(data):\n    #构造新特征\n    create_feature(data);\n    #丢弃特征\n    data.drop(to_drop,axis=1,inplace=True)\n    \n    #填充None值,因为在特征说明中,None也是某些特征的一个值,所以对于这部分特征的缺失值以None填充\n    fill_none = ['MasVnrType','BsmtExposure','GarageType','MiscFeature']\n    for col in fill_none:\n        data[col].fillna('None',inplace=True);\n        \n    #对其他缺失值进行填充,离散型特征填充众数,数值型特征填充中位数\n    na_col = data.dtypes[data.isnull().any()];\n    for col in na_col.index:\n        if na_col[col] != 'object':\n            med = data[col].median();\n            data[col].fillna(med,inplace=True);\n        else:\n            mode = data[col].mode()[0];\n            data[col].fillna(mode,inplace=True);\n    \n    #对正态偏移的特征进行正态转换,numeric_col就是数值型特征,zero_col是含有零值的数值型特征\n    #因为如果对含零特征进行转换的话会有各种各种的小问题,所以干脆单独只对非零数值进行转换\n    numeric_col = data.skew().index;\n    zero_col = data.columns[data.isin([0]).any()]\n    for col in numeric_col:\n        #对于那些condition特征,例如取值是0,1,2,3...那些我不作变换,因为意义不大\n        if len(pd.value_counts(data[col])) <= 10 : continue; \n        #如果是含有零值的特征,则只对非零值变换,至于用哪种形式变换,boxcox会自动根据数据来调整\n        if col in zero_col:       \n            trans_data = data[data>0][col];\n            before = abs(trans_data.skew());\n            cox,_ = boxcox(trans_data)\n            log_after = abs(Series(cox).skew());\n            if log_after < before:\n                data.loc[trans_data.index,col] = cox;\n        #如果是非零值的特征,则全部作转换\n        else:\n            before = abs(data[col].skew());\n            cox,_ = boxcox(data[col])\n            log_after = abs(Series(cox).skew());\n            if log_after < before:\n                data.loc[:,col] = cox;\n    #mapper值的映射转换\n    for col,mapp in mapper.items():\n        data.loc[:,col] = data[col].map(mapp);\n \n\ndf_train = pd.read_csv(os.path.join(data_dir, \"train.csv\"));\ndf_test = pd.read_csv(os.path.join(data_dir, \"test.csv\"));\ntest_ID = df_test['Id'];\n\n\n\n#去除离群点\nGrLivArea_outlier = set(df_train.index[(df_train['SalePrice']<200000)&(df_train['GrLivArea']>4000)]);\nLotFrontage_outlier = set(df_train.index[df_train['LotFrontage']>300]);\ndf_train.drop(LotFrontage_outlier|GrLivArea_outlier,inplace=True)\n\n\n\n#因为删除了几行数据,所以index的序列不再连续,需要重新reindex\ndf_train.reset_index(drop=True,inplace=True)\nprices = np.log1p(df_train.loc[:,'SalePrice'])\ndf_train.drop(['SalePrice'],axis=1,inplace=True)\n#这里对训练集和测试集进行合并,然后再进行特征工程\nall_data = pd.concat([df_train,df_test])\nall_data.reset_index(drop=True,inplace=True)\n\n#进行特征工程\nprocessing(all_data);\n\n#dummy转换\ndummy = pd.get_dummies(all_data,drop_first=True);\n\n```\n\n>模型训练：产生训练模型:\n```\n#试了Ridge,Lasso,ElasticNet以及GBM,发现ridge的表现比其他的都好,参数alpha=6是调参结果\nridge = Ridge(6);\nridge.fit(dummy.iloc[:prices.shape[0],:],prices);\n```\n\n>模型评估:RMSE\n```\n暂时没写\n```\n\n>结果预测：\n```python\nresult = np.expm1(ridge.predict(dummy.iloc[prices.shape[0]:,:]))\n```\n\n>结果导出：\n```python\npre = DataFrame(result,columns=['SalePrice'])\nprediction = pd.concat([test_ID,pre],axis=1)\nprediction.to_csv(os.path.join(data_dir, \"submission.csv\"),index=False)\n```"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/house-price/房价测试文档.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Id</th>\\n\",\n       \"      <th>MSSubClass</th>\\n\",\n       \"      <th>MSZoning</th>\\n\",\n       \"      <th>LotFrontage</th>\\n\",\n       \"      <th>LotArea</th>\\n\",\n       \"      <th>Street</th>\\n\",\n       \"      <th>Alley</th>\\n\",\n       \"      <th>LotShape</th>\\n\",\n       \"      <th>LandContour</th>\\n\",\n       \"      <th>Utilities</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>PoolArea</th>\\n\",\n       \"      <th>PoolQC</th>\\n\",\n       \"      <th>Fence</th>\\n\",\n       \"      <th>MiscFeature</th>\\n\",\n       \"      <th>MiscVal</th>\\n\",\n       \"      <th>MoSold</th>\\n\",\n       \"      <th>YrSold</th>\\n\",\n       \"      <th>SaleType</th>\\n\",\n       \"      <th>SaleCondition</th>\\n\",\n       \"      <th>SalePrice</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>RL</td>\\n\",\n       \"      <td>65.0</td>\\n\",\n       \"      <td>8450</td>\\n\",\n       \"      <td>Pave</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Reg</td>\\n\",\n       \"      <td>Lvl</td>\\n\",\n       \"      <td>AllPub</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>2008</td>\\n\",\n       \"      <td>WD</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"      <td>208500</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>20</td>\\n\",\n       \"      <td>RL</td>\\n\",\n       \"      <td>80.0</td>\\n\",\n       \"      <td>9600</td>\\n\",\n       \"      <td>Pave</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Reg</td>\\n\",\n       \"      <td>Lvl</td>\\n\",\n       \"      <td>AllPub</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>2007</td>\\n\",\n       \"      <td>WD</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"      <td>181500</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>RL</td>\\n\",\n       \"      <td>68.0</td>\\n\",\n       \"      <td>11250</td>\\n\",\n       \"      <td>Pave</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>IR1</td>\\n\",\n       \"      <td>Lvl</td>\\n\",\n       \"      <td>AllPub</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>2008</td>\\n\",\n       \"      <td>WD</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"      <td>223500</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>70</td>\\n\",\n       \"      <td>RL</td>\\n\",\n       \"      <td>60.0</td>\\n\",\n       \"      <td>9550</td>\\n\",\n       \"      <td>Pave</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>IR1</td>\\n\",\n       \"      <td>Lvl</td>\\n\",\n       \"      <td>AllPub</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>2006</td>\\n\",\n       \"      <td>WD</td>\\n\",\n       \"      <td>Abnorml</td>\\n\",\n       \"      <td>140000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>RL</td>\\n\",\n       \"      <td>84.0</td>\\n\",\n       \"      <td>14260</td>\\n\",\n       \"      <td>Pave</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>IR1</td>\\n\",\n       \"      <td>Lvl</td>\\n\",\n       \"      <td>AllPub</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>2008</td>\\n\",\n       \"      <td>WD</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"      <td>250000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 81 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\\\\n\",\n       \"0   1          60       RL         65.0     8450   Pave   NaN      Reg   \\n\",\n       \"1   2          20       RL         80.0     9600   Pave   NaN      Reg   \\n\",\n       \"2   3          60       RL         68.0    11250   Pave   NaN      IR1   \\n\",\n       \"3   4          70       RL         60.0     9550   Pave   NaN      IR1   \\n\",\n       \"4   5          60       RL         84.0    14260   Pave   NaN      IR1   \\n\",\n       \"\\n\",\n       \"  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\\\\n\",\n       \"0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \\n\",\n       \"1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \\n\",\n       \"2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \\n\",\n       \"3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \\n\",\n       \"4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \\n\",\n       \"\\n\",\n       \"  MoSold YrSold  SaleType  SaleCondition  SalePrice  \\n\",\n       \"0      2   2008        WD         Normal     208500  \\n\",\n       \"1      5   2007        WD         Normal     181500  \\n\",\n       \"2      9   2008        WD         Normal     223500  \\n\",\n       \"3      2   2006        WD        Abnorml     140000  \\n\",\n       \"4     12   2008        WD         Normal     250000  \\n\",\n       \"\\n\",\n       \"[5 rows x 81 columns]\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train = pd.read_csv('../input/train.csv')\\n\",\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Id</th>\\n\",\n       \"      <th>MSSubClass</th>\\n\",\n       \"      <th>MSZoning</th>\\n\",\n       \"      <th>LotFrontage</th>\\n\",\n       \"      <th>LotArea</th>\\n\",\n       \"      <th>Street</th>\\n\",\n       \"      <th>Alley</th>\\n\",\n       \"      <th>LotShape</th>\\n\",\n       \"      <th>LandContour</th>\\n\",\n       \"      <th>Utilities</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>ScreenPorch</th>\\n\",\n       \"      <th>PoolArea</th>\\n\",\n       \"      <th>PoolQC</th>\\n\",\n       \"      <th>Fence</th>\\n\",\n       \"      <th>MiscFeature</th>\\n\",\n       \"      <th>MiscVal</th>\\n\",\n       \"      <th>MoSold</th>\\n\",\n       \"      <th>YrSold</th>\\n\",\n       \"      <th>SaleType</th>\\n\",\n       \"      <th>SaleCondition</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1461</td>\\n\",\n       \"      <td>20</td>\\n\",\n       \"      <td>RH</td>\\n\",\n       \"      <td>80.0</td>\\n\",\n       \"      <td>11622</td>\\n\",\n       \"      <td>Pave</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Reg</td>\\n\",\n       \"      <td>Lvl</td>\\n\",\n       \"      <td>AllPub</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>120</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>MnPrv</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>2010</td>\\n\",\n       \"      <td>WD</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1462</td>\\n\",\n       \"      <td>20</td>\\n\",\n       \"      <td>RL</td>\\n\",\n       \"      <td>81.0</td>\\n\",\n       \"      <td>14267</td>\\n\",\n       \"      <td>Pave</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>IR1</td>\\n\",\n       \"      <td>Lvl</td>\\n\",\n       \"      <td>AllPub</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Gar2</td>\\n\",\n       \"      <td>12500</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>2010</td>\\n\",\n       \"      <td>WD</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1463</td>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>RL</td>\\n\",\n       \"      <td>74.0</td>\\n\",\n       \"      <td>13830</td>\\n\",\n       \"      <td>Pave</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>IR1</td>\\n\",\n       \"      <td>Lvl</td>\\n\",\n       \"      <td>AllPub</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>MnPrv</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>2010</td>\\n\",\n       \"      <td>WD</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1464</td>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>RL</td>\\n\",\n       \"      <td>78.0</td>\\n\",\n       \"      <td>9978</td>\\n\",\n       \"      <td>Pave</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>IR1</td>\\n\",\n       \"      <td>Lvl</td>\\n\",\n       \"      <td>AllPub</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>2010</td>\\n\",\n       \"      <td>WD</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1465</td>\\n\",\n       \"      <td>120</td>\\n\",\n       \"      <td>RL</td>\\n\",\n       \"      <td>43.0</td>\\n\",\n       \"      <td>5005</td>\\n\",\n       \"      <td>Pave</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>IR1</td>\\n\",\n       \"      <td>HLS</td>\\n\",\n       \"      <td>AllPub</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>144</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2010</td>\\n\",\n       \"      <td>WD</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 80 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\\\\n\",\n       \"0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \\n\",\n       \"1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \\n\",\n       \"2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \\n\",\n       \"3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \\n\",\n       \"4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \\n\",\n       \"\\n\",\n       \"  LandContour Utilities      ...       ScreenPorch PoolArea PoolQC  Fence  \\\\\\n\",\n       \"0         Lvl    AllPub      ...               120        0    NaN  MnPrv   \\n\",\n       \"1         Lvl    AllPub      ...                 0        0    NaN    NaN   \\n\",\n       \"2         Lvl    AllPub      ...                 0        0    NaN  MnPrv   \\n\",\n       \"3         Lvl    AllPub      ...                 0        0    NaN    NaN   \\n\",\n       \"4         HLS    AllPub      ...               144        0    NaN    NaN   \\n\",\n       \"\\n\",\n       \"  MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \\n\",\n       \"0         NaN       0      6    2010        WD         Normal  \\n\",\n       \"1        Gar2   12500      6    2010        WD         Normal  \\n\",\n       \"2         NaN       0      3    2010        WD         Normal  \\n\",\n       \"3         NaN       0      6    2010        WD         Normal  \\n\",\n       \"4         NaN       0      1    2010        WD         Normal  \\n\",\n       \"\\n\",\n       \"[5 rows x 80 columns]\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test = pd.read_csv('../input/test.csv')\\n\",\n    \"test.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"数值型和类别型分开处理\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dtype('int64')\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test.dtypes['Id']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['Id', 'MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']\\n\",\n      \"['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_columns = []\\n\",\n    \"cate_columns = []\\n\",\n    \"for column in test.columns:\\n\",\n    \"    if test.dtypes[column] != np.dtype('object'):\\n\",\n    \"        num_columns.append(column)\\n\",\n    \"    else:\\n\",\n    \"        cate_columns.append(column)\\n\",\n    \"print(num_columns)\\n\",\n    \"print(cate_columns)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"label = train.pop('SalePrice')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"填充缺失\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# 数值型用中值填充\\n\",\n    \"for column in num_columns:\\n\",\n    \"    train[column] = train[column].fillna(train[column].median())\\n\",\n    \"    test[column] = test[column].fillna(test[column].median())\\n\",\n    \"\\n\",\n    \"# # 类别型用最多的填充 # 线上0.13488\\n\",\n    \"# for column in cate_columns:\\n\",\n    \"#     train[column] = train[column].fillna(train[column].mode())\\n\",\n    \"#     test[column] = test[column].fillna(test[column].mode())\\n\",\n    \"    \\n\",\n    \"# 类别型填充'NaN' # 线上0.13436\\n\",\n    \"for column in cate_columns:\\n\",\n    \"    train[column] = train[column].fillna('NaN')\\n\",\n    \"    test[column] = test[column].fillna('NaN')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"类别型哑变量处理\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data = pd.concat([train,test],axis=0) #训练集要和测试集放一起\\n\",\n    \"for column in cate_columns:\\n\",\n    \"    \\n\",\n    \"    t = pd.get_dummies(data[column],prefix=column)\\n\",\n    \"    train = pd.concat([train,t[:len(train)]],axis=1)\\n\",\n    \"    train.drop(column,axis=1,inplace=True)\\n\",\n    \"    \\n\",\n    \"    test = pd.concat([test,t[len(train):]],axis=1)\\n\",\n    \"    test.drop(column,axis=1,inplace=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"回归走起\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.linear_model import Lasso,LinearRegression,Ridge,ElasticNet,TheilSenRegressor,HuberRegressor,RANSACRegressor\\n\",\n    \"from sklearn.svm import SVR\\n\",\n    \"from sklearn.tree import DecisionTreeRegressor,ExtraTreeRegressor\\n\",\n    \"from sklearn.ensemble import AdaBoostRegressor,ExtraTreesRegressor,GradientBoostingRegressor,RandomForestRegressor\\n\",\n    \"from xgboost import XGBRegressor\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.metrics import mean_squared_error\\n\",\n    \"import itertools\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train, X_test, y_train, y_test = train_test_split(train, label, test_size=0.33, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"regs = [\\n\",\n    \"    ['Lasso',Lasso()],\\n\",\n    \"    ['LinearRegression',LinearRegression()],\\n\",\n    \"    ['Ridge',Ridge()],\\n\",\n    \"    ['ElasticNet',ElasticNet()],\\n\",\n    \"    ['TheilSenRegressor',TheilSenRegressor()],\\n\",\n    \"    ['RANSACRegressor',RANSACRegressor()],\\n\",\n    \"    ['HuberRegressor',HuberRegressor()],\\n\",\n    \"    ['SVR',SVR(kernel='linear')],\\n\",\n    \"    ['DecisionTreeRegressor',DecisionTreeRegressor()],\\n\",\n    \"    ['ExtraTreeRegressor',ExtraTreeRegressor()],\\n\",\n    \"    ['AdaBoostRegressor',AdaBoostRegressor(n_estimators=150)],\\n\",\n    \"    ['ExtraTreesRegressor',ExtraTreesRegressor(n_estimators=150)],\\n\",\n    \"    ['GradientBoostingRegressor',GradientBoostingRegressor(n_estimators=150)],\\n\",\n    \"    ['RandomForestRegressor',RandomForestRegressor(n_estimators=150)],\\n\",\n    \"    ['XGBRegressor',XGBRegressor(n_estimators=150)],\\n\",\n    \"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Lasso\\n\",\n      \"LinearRegression\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\\n\",\n      \"  ConvergenceWarning)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Ridge\\n\",\n      \"ElasticNet\\n\",\n      \"TheilSenRegressor\\n\",\n      \"y_pred have 1 negative values, we fill it with np.median(y_pred)\\n\",\n      \"RANSACRegressor\\n\",\n      \"y_pred have 1 negative values, we fill it with np.median(y_pred)\\n\",\n      \"HuberRegressor\\n\",\n      \"SVR\\n\",\n      \"DecisionTreeRegressor\\n\",\n      \"ExtraTreeRegressor\\n\",\n      \"AdaBoostRegressor\\n\",\n      \"ExtraTreesRegressor\\n\",\n      \"GradientBoostingRegressor\\n\",\n      \"RandomForestRegressor\\n\",\n      \"XGBRegressor\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"preds = []\\n\",\n    \"for reg_name,reg in regs:\\n\",\n    \"    print(reg_name)\\n\",\n    \"    reg.fit(X_train,y_train)\\n\",\n    \"    y_pred = reg.predict(X_test)\\n\",\n    \"    if np.sum(y_pred<0) > 0:\\n\",\n    \"        print('y_pred have',np.sum(y_pred<0),'negative values, we fill it with np.median(y_pred)')\\n\",\n    \"        y_pred[y_pred<0] = np.median(y_pred)\\n\",\n    \"    score = np.sqrt(mean_squared_error(np.log(y_test),np.log(y_pred)))\\n\",\n    \"    preds.append([reg_name,y_pred])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model Amount : 1\\n\",\n      \"GradientBoostingRegressor 0.133400775498\\n\",\n      \"XGBRegressor 0.135452373343\\n\",\n      \"RandomForestRegressor 0.146986410514\\n\",\n      \"ExtraTreesRegressor 0.163995003619\\n\",\n      \"Lasso 0.164197374564\\n\",\n      \"Ridge 0.165564495344\\n\",\n      \"ElasticNet 0.173365651563\\n\",\n      \"LinearRegression 0.176583028477\\n\",\n      \"SVR 0.19083071496\\n\",\n      \"TheilSenRegressor 0.191042908374\\n\",\n      \"RANSACRegressor 0.192942079892\\n\",\n      \"AdaBoostRegressor 0.204862821167\\n\",\n      \"DecisionTreeRegressor 0.211129131696\\n\",\n      \"HuberRegressor 0.230319112049\\n\",\n      \"ExtraTreeRegressor 0.232806183519\\n\",\n      \"\\n\",\n      \"Model Amount : 2\\n\",\n      \"Lasso+XGBRegressor 0.127194837142\\n\",\n      \"Lasso+GradientBoostingRegressor 0.127648646599\\n\",\n      \"LinearRegression+XGBRegressor 0.129854978109\\n\",\n      \"LinearRegression+GradientBoostingRegressor 0.130322414269\\n\",\n      \"Lasso+RandomForestRegressor 0.131777367574\\n\",\n      \"Ridge+GradientBoostingRegressor 0.132674339351\\n\",\n      \"Ridge+XGBRegressor 0.132717636512\\n\",\n      \"GradientBoostingRegressor+XGBRegressor 0.133092668715\\n\",\n      \"TheilSenRegressor+GradientBoostingRegressor 0.134189654069\\n\",\n      \"LinearRegression+RandomForestRegressor 0.134459423562\\n\",\n      \"TheilSenRegressor+XGBRegressor 0.13452849859\\n\",\n      \"Ridge+RandomForestRegressor 0.135965557993\\n\",\n      \"GradientBoostingRegressor+RandomForestRegressor 0.136722351754\\n\",\n      \"RandomForestRegressor+XGBRegressor 0.137379778459\\n\",\n      \"RANSACRegressor+XGBRegressor 0.137778252354\\n\",\n      \"RANSACRegressor+GradientBoostingRegressor 0.13887130292\\n\",\n      \"TheilSenRegressor+RandomForestRegressor 0.138939845235\\n\",\n      \"Lasso+ExtraTreesRegressor 0.139028201472\\n\",\n      \"ElasticNet+GradientBoostingRegressor 0.140793992378\\n\",\n      \"ElasticNet+XGBRegressor 0.141269235288\\n\",\n      \"LinearRegression+ExtraTreesRegressor 0.141278575302\\n\",\n      \"ExtraTreesRegressor+GradientBoostingRegressor 0.142456173756\\n\",\n      \"RANSACRegressor+RandomForestRegressor 0.142529450531\\n\",\n      \"ExtraTreesRegressor+XGBRegressor 0.142661094906\\n\",\n      \"Lasso+ElasticNet 0.143739306229\\n\",\n      \"SVR+GradientBoostingRegressor 0.144334394399\\n\",\n      \"SVR+XGBRegressor 0.144429354811\\n\",\n      \"Lasso+SVR 0.144839874141\\n\",\n      \"Ridge+ExtraTreesRegressor 0.145533921197\\n\",\n      \"TheilSenRegressor+ExtraTreesRegressor 0.145762688821\\n\",\n      \"ElasticNet+RandomForestRegressor 0.145930647279\\n\",\n      \"LinearRegression+ElasticNet 0.14714366104\\n\",\n      \"Ridge+SVR 0.148675979762\\n\",\n      \"RANSACRegressor+ExtraTreesRegressor 0.149035532702\\n\",\n      \"LinearRegression+SVR 0.149463974892\\n\",\n      \"ExtraTreesRegressor+RandomForestRegressor 0.150897032771\\n\",\n      \"ElasticNet+RANSACRegressor 0.151238088126\\n\",\n      \"Lasso+DecisionTreeRegressor 0.151435568567\\n\",\n      \"ElasticNet+ExtraTreesRegressor 0.151484233334\\n\",\n      \"SVR+RandomForestRegressor 0.151802505259\\n\",\n      \"Lasso+AdaBoostRegressor 0.152727177296\\n\",\n      \"Ridge+DecisionTreeRegressor 0.153349579532\\n\",\n      \"LinearRegression+AdaBoostRegressor 0.153588796404\\n\",\n      \"SVR+ExtraTreesRegressor 0.153773908357\\n\",\n      \"TheilSenRegressor+SVR 0.154020790332\\n\",\n      \"Ridge+ElasticNet 0.154222928805\\n\",\n      \"HuberRegressor+GradientBoostingRegressor 0.155171883334\\n\",\n      \"HuberRegressor+XGBRegressor 0.155421011111\\n\",\n      \"Lasso+Ridge 0.155731170786\\n\",\n      \"TheilSenRegressor+RANSACRegressor 0.155745389175\\n\",\n      \"Ridge+TheilSenRegressor 0.156467195378\\n\",\n      \"ElasticNet+TheilSenRegressor 0.15663707817\\n\",\n      \"DecisionTreeRegressor+GradientBoostingRegressor 0.156878250168\\n\",\n      \"Lasso+HuberRegressor 0.156936725358\\n\",\n      \"DecisionTreeRegressor+XGBRegressor 0.156984998134\\n\",\n      \"Ridge+HuberRegressor 0.157458812217\\n\",\n      \"LinearRegression+DecisionTreeRegressor 0.157658699295\\n\",\n      \"Ridge+AdaBoostRegressor 0.158269919879\\n\",\n      \"TheilSenRegressor+DecisionTreeRegressor 0.159291493516\\n\",\n      \"AdaBoostRegressor+GradientBoostingRegressor 0.159476695957\\n\",\n      \"Ridge+RANSACRegressor 0.159712355716\\n\",\n      \"TheilSenRegressor+AdaBoostRegressor 0.159890970839\\n\",\n      \"AdaBoostRegressor+XGBRegressor 0.160207457994\\n\",\n      \"LinearRegression+TheilSenRegressor 0.160341205153\\n\",\n      \"RANSACRegressor+SVR 0.160352896285\\n\",\n      \"LinearRegression+HuberRegressor 0.160413162684\\n\",\n      \"Lasso+TheilSenRegressor 0.160669786588\\n\",\n      \"RANSACRegressor+AdaBoostRegressor 0.161124399361\\n\",\n      \"LinearRegression+Ridge 0.16140737581\\n\",\n      \"Lasso+RANSACRegressor 0.161466311938\\n\",\n      \"RANSACRegressor+DecisionTreeRegressor 0.161718787176\\n\",\n      \"TheilSenRegressor+HuberRegressor 0.162084000758\\n\",\n      \"ElasticNet+DecisionTreeRegressor 0.162954948425\\n\",\n      \"ExtraTreeRegressor+GradientBoostingRegressor 0.16391025733\\n\",\n      \"HuberRegressor+RandomForestRegressor 0.164903671718\\n\",\n      \"ExtraTreeRegressor+XGBRegressor 0.165114883621\\n\",\n      \"DecisionTreeRegressor+RandomForestRegressor 0.165273826908\\n\",\n      \"HuberRegressor+ExtraTreesRegressor 0.165623710284\\n\",\n      \"Lasso+ExtraTreeRegressor 0.166008878878\\n\",\n      \"ElasticNet+AdaBoostRegressor 0.166014141401\\n\",\n      \"LinearRegression+RANSACRegressor 0.166404470152\\n\",\n      \"Lasso+LinearRegression 0.166758170725\\n\",\n      \"SVR+DecisionTreeRegressor 0.167077526763\\n\",\n      \"SVR+AdaBoostRegressor 0.167284448134\\n\",\n      \"TheilSenRegressor+ExtraTreeRegressor 0.167491407559\\n\",\n      \"LinearRegression+ExtraTreeRegressor 0.167894255825\\n\",\n      \"DecisionTreeRegressor+ExtraTreesRegressor 0.167947156153\\n\",\n      \"Ridge+ExtraTreeRegressor 0.168542361882\\n\",\n      \"AdaBoostRegressor+RandomForestRegressor 0.168634913239\\n\",\n      \"RANSACRegressor+ExtraTreeRegressor 0.170671156869\\n\",\n      \"ExtraTreeRegressor+RandomForestRegressor 0.170815887983\\n\",\n      \"ElasticNet+SVR 0.171791460365\\n\",\n      \"ElasticNet+ExtraTreeRegressor 0.172058735931\\n\",\n      \"AdaBoostRegressor+ExtraTreesRegressor 0.174525027385\\n\",\n      \"SVR+ExtraTreeRegressor 0.17503544538\\n\",\n      \"ExtraTreeRegressor+ExtraTreesRegressor 0.176785179871\\n\",\n      \"ElasticNet+HuberRegressor 0.176930865209\\n\",\n      \"RANSACRegressor+HuberRegressor 0.177538361764\\n\",\n      \"HuberRegressor+DecisionTreeRegressor 0.178652706775\\n\",\n      \"HuberRegressor+AdaBoostRegressor 0.179775917092\\n\",\n      \"DecisionTreeRegressor+AdaBoostRegressor 0.183158779112\\n\",\n      \"HuberRegressor+ExtraTreeRegressor 0.184983904469\\n\",\n      \"DecisionTreeRegressor+ExtraTreeRegressor 0.187241512071\\n\",\n      \"ExtraTreeRegressor+AdaBoostRegressor 0.188826296636\\n\",\n      \"HuberRegressor+SVR 0.201144832431\\n\",\n      \"\\n\",\n      \"Model Amount : 3\\n\",\n      \"Lasso+GradientBoostingRegressor+XGBRegressor 0.124979582373\\n\",\n      \"LinearRegression+GradientBoostingRegressor+XGBRegressor 0.126070603112\\n\",\n      \"Lasso+GradientBoostingRegressor+RandomForestRegressor 0.127349903462\\n\",\n      \"Lasso+RandomForestRegressor+XGBRegressor 0.12736301581\\n\",\n      \"LinearRegression+RandomForestRegressor+XGBRegressor 0.128456964708\\n\",\n      \"LinearRegression+GradientBoostingRegressor+RandomForestRegressor 0.128460717855\\n\",\n      \"Ridge+GradientBoostingRegressor+XGBRegressor 0.128869822618\\n\",\n      \"TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.129256345946\\n\",\n      \"Ridge+GradientBoostingRegressor+RandomForestRegressor 0.130631927178\\n\",\n      \"Ridge+RandomForestRegressor+XGBRegressor 0.130822051512\\n\",\n      \"Lasso+ExtraTreesRegressor+XGBRegressor 0.13101688532\\n\",\n      \"Lasso+SVR+XGBRegressor 0.131062962077\\n\",\n      \"Lasso+ExtraTreesRegressor+GradientBoostingRegressor 0.131174046484\\n\",\n      \"Lasso+ElasticNet+XGBRegressor 0.131352935978\\n\",\n      \"Lasso+SVR+GradientBoostingRegressor 0.131388980045\\n\",\n      \"RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.1315453077\\n\",\n      \"Lasso+ElasticNet+GradientBoostingRegressor 0.131575408013\\n\",\n      \"TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131601465568\\n\",\n      \"TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.131921433551\\n\",\n      \"LinearRegression+ExtraTreesRegressor+XGBRegressor 0.131985026291\\n\",\n      \"LinearRegression+ExtraTreesRegressor+GradientBoostingRegressor 0.13215564798\\n\",\n      \"LinearRegression+ElasticNet+XGBRegressor 0.132774799511\\n\",\n      \"LinearRegression+SVR+XGBRegressor 0.133008105872\\n\",\n      \"LinearRegression+ElasticNet+GradientBoostingRegressor 0.133033328397\\n\",\n      \"Lasso+Ridge+XGBRegressor 0.133036107338\\n\",\n      \"LinearRegression+SVR+GradientBoostingRegressor 0.133345358091\\n\",\n      \"Lasso+Ridge+GradientBoostingRegressor 0.1334359391\\n\",\n      \"RANSACRegressor+RandomForestRegressor+XGBRegressor 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\"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145113554733\\n\",\n      \"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145117846624\\n\",\n      \"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145170690458\\n\",\n      \"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145190059566\\n\",\n      \"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145205338705\\n\",\n      \"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145244608209\\n\",\n      \"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.145248008842\\n\",\n      \"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145248369791\\n\",\n      \"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145258390834\\n\",\n      \"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.145273272561\\n\",\n      \"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145323254845\\n\",\n      \"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.145347104887\\n\",\n      \"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145357185877\\n\",\n      \"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145376304952\\n\",\n      \"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145376562237\\n\",\n      \"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145391562092\\n\",\n      \"ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145415444388\\n\",\n      \"SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145430462164\\n\",\n      \"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14550926391\\n\",\n      \"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145558225792\\n\",\n      \"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145579547053\\n\",\n      \"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.145594455299\\n\",\n      \"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14566371985\\n\",\n      \"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 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\"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145848201022\\n\",\n      \"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145854969415\\n\",\n      \"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145910944872\\n\",\n      \"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145923886832\\n\",\n      \"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.146027185705\\n\",\n      \"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.146051124774\\n\",\n      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\"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.147456226863\\n\",\n      \"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147481625282\\n\",\n      \"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14756747949\\n\",\n      \"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147656665835\\n\",\n      \"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147906488044\\n\",\n      \"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.148196480443\\n\",\n      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\"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142975220497\\n\",\n      \"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142975458584\\n\",\n      \"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142976037768\\n\",\n      \"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142995944969\\n\",\n      \"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143077399004\\n\",\n      \"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143115618661\\n\",\n      \"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143140458639\\n\",\n      \"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143163990529\\n\",\n      \"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143180457899\\n\",\n      \"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143194491296\\n\",\n      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0.143671382095\\n\",\n      \"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143706817135\\n\",\n      \"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143707711832\\n\",\n      \"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143724076562\\n\",\n      \"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143729858807\\n\",\n      \"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143737905074\\n\",\n      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\"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143856992632\\n\",\n      \"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143864539445\\n\",\n      \"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143868645341\\n\",\n      \"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143875390557\\n\",\n      \"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143997634798\\n\",\n      \"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144001436907\\n\",\n      \"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.144154018\\n\",\n      \"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144174574745\\n\",\n      \"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14420991222\\n\",\n      \"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144214389739\\n\",\n      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\"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144470684069\\n\",\n      \"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144822023744\\n\",\n      \"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144828082935\\n\",\n      \"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144864220659\\n\",\n      \"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145021362518\\n\",\n      \"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145052438767\\n\",\n      \"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145134296097\\n\",\n      \"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.145277297891\\n\",\n      \"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145358023191\\n\",\n      \"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145812471188\\n\",\n      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\"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12941602922\\n\",\n      \"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129636396187\\n\",\n      \"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12968893155\\n\",\n      \"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129733299025\\n\",\n      \"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129762839682\\n\",\n      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\"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137071586047\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137102123159\\n\",\n      \"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137128030885\\n\",\n      \"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13716437238\\n\",\n      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\"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137725446929\\n\",\n      \"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137759426105\\n\",\n      \"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137809036417\\n\",\n      \"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137812312562\\n\",\n      \"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137818205418\\n\",\n      \"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137822558705\\n\",\n      \"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137848103475\\n\",\n      \"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137908397167\\n\",\n      \"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137934621573\\n\",\n      \"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137968304991\\n\",\n      \"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137996068654\\n\",\n      \"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13799818074\\n\",\n      \"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138012658736\\n\",\n      \"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138022106171\\n\",\n      \"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138050109352\\n\",\n      \"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138056995574\\n\",\n      \"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138105167717\\n\",\n      \"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138214029518\\n\",\n      \"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138234034747\\n\",\n      \"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138342452168\\n\",\n      \"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138509472229\\n\",\n      \"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138527787703\\n\",\n      \"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13871382797\\n\",\n      \"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138971504659\\n\",\n      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\"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132532657022\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132549621322\\n\",\n      \"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132577342995\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132629708936\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132663584365\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132727464185\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132770889254\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132930442099\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132952803958\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133122022177\\n\",\n      \"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133158182673\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133181989475\\n\",\n      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\"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134363976655\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134440532945\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134495221371\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134497289028\\n\",\n      \"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134569368771\\n\",\n      \"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134598241817\\n\",\n      \"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13476875638\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134785045278\\n\",\n      \"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13481650035\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13483166423\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134839715335\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134848686299\\n\",\n      \"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134848971186\\n\",\n      \"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134855399278\\n\",\n      \"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134884028204\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134885410562\\n\",\n      \"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134887781986\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134922975665\\n\",\n      \"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135002247468\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135061987974\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13509486715\\n\",\n      \"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135149110024\\n\",\n      \"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135196483059\\n\",\n      \"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135233692653\\n\",\n      \"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135255513957\\n\",\n      \"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135257215623\\n\",\n      \"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135279046154\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135311373291\\n\",\n      \"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135325034018\\n\",\n      \"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135337163676\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13533944101\\n\",\n      \"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135390496481\\n\",\n      \"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135408213767\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135490057998\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13553414473\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135542236337\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13556195123\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135711723019\\n\",\n      \"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135831474658\\n\",\n      \"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135975066796\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13598996842\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136013733726\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136062039657\\n\",\n      \"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136089990252\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13610516966\\n\",\n      \"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13612478312\\n\",\n      \"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13630949713\\n\",\n      \"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13640080747\\n\",\n      \"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136427992131\\n\",\n      \"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136463815399\\n\",\n      \"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136489587773\\n\",\n      \"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13649925935\\n\",\n      \"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136551708233\\n\",\n      \"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136574163577\\n\",\n      \"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136598586988\\n\",\n      \"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136601332209\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136715094522\\n\",\n      \"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137081400282\\n\",\n      \"\\n\",\n      \"Model Amount : 14\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132828091395\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132850505043\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133168768482\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13375435832\\n\",\n      \"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133930537605\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134068840053\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134138819305\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13418412404\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134739257293\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134821372402\\n\",\n      \"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134836383376\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135187496405\\n\",\n      \"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135218981737\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135221327055\\n\",\n      \"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135315522158\\n\",\n      \"\\n\",\n      \"Model Amount : 15\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134028947124\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"final_results = []\\n\",\n    \"for comb_length in range(1,len(regs)+1):\\n\",\n    \"    print('Model Amount :',comb_length)\\n\",\n    \"    results = []\\n\",\n    \"    for comb in itertools.combinations(preds,comb_length):\\n\",\n    \"        pred_sum = 0\\n\",\n    \"        model_name = []\\n\",\n    \"        for reg_name,pred in comb:\\n\",\n    \"            pred_sum += pred\\n\",\n    \"            model_name.append(reg_name)\\n\",\n    \"        pred_sum /= comb_length\\n\",\n    \"        model_name = '+'.join(model_name)\\n\",\n    \"        score = np.sqrt(mean_squared_error(np.log(y_test),np.log(pred_sum)))\\n\",\n    \"        results.append([model_name,score])\\n\",\n    \"    results = sorted(results,key=lambda x:x[1])\\n\",\n    \"    for model_name,score in results:\\n\",\n    \"        print(model_name,score)\\n\",\n    \"    print()\\n\",\n    \"    final_results.append(results[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Lasso+GradientBoostingRegressor+XGBRegressor 0.124979582373\\n\",\n      \"Lasso+LinearRegression+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.126491410988\\n\",\n      \"Lasso+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.126842572869\\n\",\n      \"Lasso+XGBRegressor 0.127194837142\\n\",\n      \"Lasso+LinearRegression+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127371402793\\n\",\n      \"Lasso+LinearRegression+Ridge+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127926929743\\n\",\n      \"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128744815807\\n\",\n      \"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12914274306\\n\",\n      \"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12941602922\\n\",\n      \"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129702489557\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130193380744\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131609901609\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132828091395\\n\",\n      \"GradientBoostingRegressor 0.133400775498\\n\",\n      \"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134028947124\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"final_results = sorted(final_results,key=lambda x:x[1])\\n\",\n    \"for model_name,score in final_results:\\n\",\n    \"    print(model_name,score)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"最终输出\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 'Lasso'),\\n\",\n       \" (1, 'LinearRegression'),\\n\",\n       \" (2, 'Ridge'),\\n\",\n       \" (3, 'ElasticNet'),\\n\",\n       \" (4, 'TheilSenRegressor'),\\n\",\n       \" (5, 'RANSACRegressor'),\\n\",\n       \" (6, 'HuberRegressor'),\\n\",\n       \" (7, 'SVR'),\\n\",\n       \" (8, 'DecisionTreeRegressor'),\\n\",\n       \" (9, 'ExtraTreeRegressor'),\\n\",\n       \" (10, 'AdaBoostRegressor'),\\n\",\n       \" (11, 'ExtraTreesRegressor'),\\n\",\n       \" (12, 'GradientBoostingRegressor'),\\n\",\n       \" (13, 'RandomForestRegressor'),\\n\",\n       \" (14, 'XGBRegressor')]\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"[b for b in zip(itertools.count(),[a[0] for a in regs])]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pred = np.mean(list(map(lambda x:regs[x][1].predict(test),[0,12,14])),axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sub = pd.DataFrame({'Id':test['Id'],'SalePrice':pred})\\n\",\n    \"sub.to_csv('submission_Universe_fillNaN.csv',index=None)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/titanic/README.md",
    "content": "# **泰坦尼克号**\n\n## 比赛说明\n\n* [**泰坦尼克号**](https://www.kaggle.com/c/titanic) 的沉没是历史上最臭名昭着的沉船之一。1912年4月15日，在首航期间，泰坦尼克号撞上一座冰山后沉没，2224名乘客和机组人员中有1502人遇难。这一耸人听闻的悲剧震撼了国际社会，并导致了更好的船舶安全条例。\n* 沉船导致生命损失的原因之一是乘客和船员没有足够的救生艇。虽然幸存下来的运气有一些因素，但有些人比其他人更有可能生存，比如妇女，儿童和上层阶级。\n* 在这个挑战中，我们要求你完成对什么样的人可能生存的分析。特别是，我们要求你运用机器学习的工具来预测哪些乘客幸存下来的悲剧。\n\n## 参赛成员\n\n* 开源组织: [ApacheCN ~ apachecn.org](http://www.apachecn.org/)\n* **菜鸡互啄(团队)**: [@那伊抹微笑](https://github.com/wangyangting), [@风风](https://github.com/fengfengtzp), [@李铭哲](https://github.com/limingzhe), [@刘海飞](https://github.com/WindZQ), [@DL - 小王子](https://github.com/VPrincekin), [@成飘飘](https://github.com/chengpiaopiao)\n* **瑶瑶亲卫队(团队)**: [@瑶妹](https://github.com/chenyyx) 张俊皓 kngines [@谈笑风生](https://github.com/zhu1040028623) Yukine ~守护 程威 屋檐听雨 Gladiator 吃着狗粮的电酱PRPR\n\n## 比赛分析\n\n* 分类问题：预测的是`生`与`死`的问题\n* 常用算法： `K紧邻(knn)`、`逻辑回归(LogisticRegression)`、`随机森林(RandomForest)`、`支持向量机(SVM)`、`xgboost`、`GBDT`\n\n```\n步骤:\n一. 数据分析\n1. 下载并加载数据\n2. 总体预览:了解每列数据的含义,数据的格式等\n3. 数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备\n\n二. 特征工程\n1.根据业务,常识,以及第二步的数据分析构造特征工程.\n2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)\n\n三. 模型选择\n1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.\n2.比较各个模型的分数,然后取效果较好的模型作为基础模型.\n\n四. 模型融合\n1. Bagging:   同一模型的投票选举\n2. Boosting:  同一模型的再学习\n3. Voting:    不同模型的投票选举\n4. Stacking:  分层预测 – K-1份数据预测1份模型拼接，得到 预测结果*算法数（作为特征） => 从而预测最终结果\n5. Blending:  分层预测 – 将数据分成2部分（A部分训练B部分得到预测结果），得到 预测结果*算法数（作为特征） => 从而预测最终结果\n\n五. 参数优化\n1.可以通过添加或者修改特征,提高模型的上限.\n2.通过修改模型的参数,是模型逼近上限\n```\n\n## 一. 数据分析\n\n### 数据下载和观察\n\n* 数据集下载地址：<https://www.kaggle.com/c/titanic/data>\n\n> 特征说明\n\n| 特征 | 描述 | 值|\n| - | - | - |\n| PassengerId | 乘客ID      | |\n| Survived | 生存           | 0 = No, 1 = Yes |\n| Pclass   | 票类别-社会地位 | 1 = 1st, 2 = 2nd, 3 = 3rd |  \n| Name     | 姓名           | |  \n| Sex      | 性别           | |\n| Age      | 年龄           | |    \n| SibSp    | 兄弟姐妹/配偶   | | \n| Parch    | 父母/孩子的数量 | |\n| Ticket   | 票号           | |   \n| Fare     | 乘客票价       | |  \n| Cabin    | 客舱号码       | |    \n| Embarked | 登船港口       | C=Cherbourg, Q=Queenstown, S=Southampton |  \n\n### 特征详情\n\n```python\n# 导入相关数据包\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline\n```\n\n```python\nroot_path = '/opt/data/datasets/getting-started/titanic/input'\n\ntrain = pd.read_csv('%s/%s' % (root_path, 'train.csv'))\ntest = pd.read_csv('%s/%s' % (root_path, 'test.csv'))\n```\n\n```python\ntrain.head(5)\n```\n\n![](/img/competitions/getting-started/titanic/titanic_top_5.jpg)\n\n```py\n>>> # 返回每列列名,该列非nan值个数,以及该列类型\n>>> train.info()\n>>> # test.info()\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId    891 non-null int64\nSurvived       891 non-null int64\nPclass         891 non-null int64\nName           891 non-null object\nSex            891 non-null object\nAge            714 non-null float64\nSibSp          891 non-null int64\nParch          891 non-null int64\nTicket         891 non-null object\nFare           891 non-null float64\nCabin          204 non-null object\nEmbarked       889 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.6+ KB\n```\n\n```py\n>>> # 返回数值型变量的统计量\n>>> # train.describe(percentiles=[0.00, 0.25, 0.5, 0.75, 1.00])\n>>> print(titanic.describe())\n        PassengerId    Survived      Pclass         Age       SibSp       Parch        Fare\ncount   891.000000  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\nmean    446.000000    0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\nstd     257.353842    0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\nmin       1.000000    0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n25%     223.500000    0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n50%     446.000000    0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n75%     668.500000    1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\nmax     891.000000    1.000000    3.000000   80.000000    8.000000    6.000000  512.329200\n```\n\n## 二. 特征工程\n\n### 特征处理\n\n目的:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备\n\n```python\n# 存活人数\ntrain['Survived'].value_counts()\n\n0    549\n1    342\nName: Survived, dtype: int64\n\n\n# 对缺失值处理（Age 中位数不错）\ntitanic[\"Age\"] = titanic[\"Age\"].fillna(titanic[\"Age\"].median())\ntitanic[\"Fare\"] = titanic[\"Fare\"].fillna(titanic[\"Fare\"].median())\n\n\n# 对文本特征进行处理（性别， 登船港口）\nprint(titanic[\"Sex\"].unique())\ntitanic.loc[titanic[\"Sex\"]==\"male\", \"Sex\"] = 0\ntitanic.loc[titanic[\"Sex\"]==\"female\", \"Sex\"] = 1\n\n# 组合特征(特征组合相关性变差了)\n# titanic[\"FamilySize\"] = titanic[\"SibSp\"] + titanic[\"Parch\"]\n\n# S的概率最大，当然我们也可以按照概率随机算，都可以\nprint(titanic[\"Embarked\"].unique())\n\"\"\"\ntitanic[[\"Embarked\"]].groupby(\"Embarked\").agg({\"Embarked\": \"count\"})\n            Embarked\nEmbarked          \nC              168\nQ               77\nS              644\n\"\"\"\ntitanic[\"Embarked\"] = titanic[\"Embarked\"].fillna('S')\ntitanic.loc[titanic[\"Embarked\"] == \"S\", \"Embarked\"] = 0\ntitanic.loc[titanic[\"Embarked\"] == \"C\", \"Embarked\"] = 1\ntitanic.loc[titanic[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n\n\ndef get_title(name):\n    # 名字的尊称\n    title_search = re.search(' ([A-Za-z]+)\\.', name)\n    if title_search:\n        return title_search.group(1)\n    return \"\"\ntitles = titanic[\"Name\"].apply(get_title)\n# print(pandas.value_counts(titles))\n# 对尊称建立mapping字典\n# 在数据的Name项中包含了对该乘客的称呼，如Mr、Miss等，这些信息包含了乘客的年龄、性别、也有可能包含社会地位，如Dr、Lady、Major、Master等称呼。这一项不方便用图表展示，但是在特征工程中，我们会将其提取出来,然后放到模型中。\n# 剩余因素还有船票价格、船舱号和船票号，这三个因素都可能会影响乘客在船中的位置从而影响逃生顺序，但是因为这三个因素与生存之间看不出明显规律，所以在后期模型融合时，将这些因素交给模型来决定其重要性。\ntitle_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Dr\": 5, \"Rev\": 6, \"Major\": 7, \"Col\": 7, \"Mlle\": 8, \"Mme\": 8, \"Don\": 9, \"Dona\": 9, \"Lady\": 10, \"Countess\": 10, \"Jonkheer\": 10, \"Sir\": 9, \"Capt\": 7, \"Ms\": 2}\nfor k, v in title_mapping.items():\n    titles[titles == k] = v\n# print(pd.value_counts(titles))\n\n\n# 添加一个新特征表示拥护尊称\ntitanic[\"Title\"] = [int(i) for i in titles.values.tolist()]\n# 添加一个新特征表示名字长度\ntitanic[\"NameLength\"] = titanic[\"Name\"].apply(lambda x: len(x))\n\n\n# 相关性太差，删除\n# titanic.drop(['PassengerId'], axis=1,inplace=True)\ntitanic.drop(['Cabin'], axis=1,inplace=True)\ntitanic.drop(['SibSp'], axis=1,inplace=True)\n# titanic.drop(['Parch'],axis=1,inplace=True)\ntitanic.drop(['Ticket'], axis=1,inplace=True)\ntitanic.drop(['Name'],   axis=1,inplace=True)\n```\n\n### 特征相关性\n\n> 1)数值型数据协方差 corr()函数\n\n来个总览,快速了解个数据的相关性\n\n```python\n# 相关性协方差表,corr()函数,返回结果接近0说明无相关性,大于0说明是正相关,小于0是负相关.\ntrain_corr = train.corr()\ntrain_corr\n```\n\n|    相关性   |  Survived  |    Pclass  |       Sex |       Age |     Parch  |      Fare |  Embarked |     Title | NameLength | \n|   ------   |   ------   |   ------   |   ------  |   ------  |   ------   |   ------  |   ------  |   ------  |  ------    |\n| Survived   |  1.000000  | -0.338481  |  0.543351 | -0.064910 |  0.081629  |  0.257307 |  0.106811 |  0.354072 |   0.332350 |\n| Pclass     | -0.338481  |  1.000000  | -0.131900 | -0.339898 |  0.018443  | -0.549500 |  0.045702 | -0.211552 |  -0.220001 |\n| Sex        |  0.543351  | -0.131900  |  1.000000 | -0.081163 |  0.245489  |  0.182333 |  0.116569 |  0.419760 |   0.448759 |\n| Age        | -0.064910  | -0.339898  | -0.081163 |  1.000000 | -0.172482  |  0.096688 | -0.009165 | -0.037174 |   0.039702 |\n| Parch      |  0.081629  |  0.018443  |  0.245489 | -0.172482 |  1.000000  |  0.216225 | -0.078665 |  0.235164 |   0.252282 |\n| Fare       |  0.257307  | -0.549500  |  0.182333 |  0.096688 |  0.216225  |  1.000000 |  0.062142 |  0.122872 |   0.155832 |\n| Embarked   |  0.106811  |  0.045702  |  0.116569 | -0.009165 | -0.078665  |  0.062142 |  1.000000 |  0.055788 |  -0.107749 |\n| Title      |  0.354072  | -0.211552  |  0.419760 | -0.037174 |  0.235164  |  0.122872 |  0.055788 |  1.000000 |   0.436099 |\n| NameLength |  0.332350  | -0.220001  |  0.448759 |  0.039702 |  0.252282  |  0.155832 | -0.107749 |  0.436099 |   1.000000 |\n\n```python\n# 画出相关性热力图\na = plt.subplots(figsize=(15,9))#调整画布大小\na = sns.heatmap(train_corr, vmin=-1, vmax=1 , annot=True , square=True)#画热力图\n```\n\n![png](/img/competitions/getting-started/titanic/titanic_corr_analysis.png)\n\n\n### 特征标准化和降维\n\n* 数据标准化\n\n1. 线性模型需要用标准化的数据建模, 而树类模型不需要标准化的数据\n2. 处理标准化的时候,注意将测试集的数据transform到test集上\n\n```py\ndef do_FeatureEngineering(data, COMPONENT_NUM=0.9):\n    # scale values  对一化\n    scaler = preprocessing.StandardScaler()\n    s_data = scaler.fit_transform(data)\n    return s_data\n\n    # # 降维(不降维，准确率还上升了)\n    # '''\n    # 使用说明：https://www.cnblogs.com/pinard/p/6243025.html\n    # n_components>=1\n    #   n_components=NUM   设置占特征数量比\n    # 0 < n_components < 1\n    #   n_components=0.99  设置阈值总方差占比\n    # '''\n    # pca = PCA(n_components=COMPONENT_NUM, whiten=False)\n    # pca.fit(s_data)  # Fit the model with X\n    # pca_data = pca.transform(s_data)  # Fit the model with X and 在X上完成降维.\n\n    # # pca 方差大小、方差占比、特征数量\n    # # print(\"方差大小:\\n\", pca.explained_variance_, \"方差占比:\\n\", pca.explained_variance_ratio_)\n    # print(\"特征数量: %s\" % pca.n_components_)\n    # print(\"总方差占比: %s\" % sum(pca.explained_variance_ratio_))\n\n    # return pca_data\n```\n\n\n## 三. 建立模型\n\n### 模型发现\n\n1. 可选单个模型模型有逻辑回归, 随机森林, svm, xgboost, gbdt等.\n2. 也可以将多个模型组合起来,进行模型融合,比如voting,stacking等方法\n3. 好的特征决定模型上限,好的模型和参数可以无线逼近上限.\n4. 我测试了多种模型,模型结果最高的随机森林,最高有0.8.\n\n### 构建模型\n\n```py\n# 0.8069524400247253 [0.79329609 0.81564246 0.8258427  0.80337079 0.79661017]\nmodel = LogisticRegression(random_state=1)\n\n# 0.8272091118939124 [0.82122905 0.80446927 0.84831461 0.82022472 0.84180791]\nmodel = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2)\n\n# 0.822670577600365  [0.82681564 0.82122905 0.83146067 0.80898876 0.82485876]\nmodel = RandomForestClassifier(random_state=1, n_estimators=50, min_samples_split=8, min_samples_leaf=4)\n\n# 0.8294499549079417   [0.82122905 0.80446927 0.86516854 0.82022472 0.83615819]\nmodel = XGBClassifier(n_estimators=196, max_depth=4, learning_rate=0.03)\n```\n\n## 四. 模型融合\n\n```py\nprint(\"模型融合\")\n\"\"\"\nBagging:   同一模型的投票选举\nBoosting:  同一模型的再学习\nVoting:    不同模型的投票选举\nStacking:  分层预测 – K-1份数据预测1份模型拼接，得到 预测结果*算法数（作为特征） => 从而预测最终结果\nBlending:  分层预测 – 将数据分成2部分（A部分训练B部分得到预测结果），得到 预测结果*算法数（作为特征） => 从而预测最终结果\n\"\"\"\n# 1. Bagging 算法实现\n# 0.8691726623564537  [0.86179183 0.82700922 0.8855615  0.87700535 0.89449541]\nmodel = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2)\n\n# 2. Boosting 算法实现\n# 0.8488710896477386  [0.8198946  0.82285903 0.87780749 0.84906417 0.87473017]\nmodel = AdaBoostClassifier(random_state=1, n_estimators=100, learning_rate=1)\n\n# 3. Voting\n# 0.8695399796790022  [0.87259552 0.8370224  0.87433155 0.86885027 0.89490016]\nmodel = VotingClassifier(\n    estimators=[\n        ('log_clf', LogisticRegression()),\n        ('ab_clf', AdaBoostClassifier()),\n        ('svm_clf', SVC(probability=True)),\n        ('rf_clf', RandomForestClassifier()),\n        ('gbdt_clf', GradientBoostingClassifier()),\n        ('rb_clf', AdaBoostClassifier())\n    ], voting='soft') # , voting='hard')\nscores = cross_val_score(model, trainData, trainLabel, cv=5, scoring='roc_auc')\nprint(scores.mean(), \"\\n\", scores)\n\n# 4. Stacking\n# 0.8713813265814722  [0.87747036 0.83886693 0.86590909 0.87085561 0.90380464]\nclfs = [\n    AdaBoostClassifier(),\n    SVC(probability=True),\n    AdaBoostClassifier(),\n    LogisticRegression(C=0.1,max_iter=100),\n    XGBClassifier(max_depth=6,n_estimators=100,num_round = 5),\n    RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True),\n    GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)\n]\n\nkf = KFold(n_splits=5, shuffle=True, random_state=1)\n\n# 创建零矩阵\ndataset_stacking_train = np.zeros((trainData.shape[0], len(clfs)))\n# dataset_stacking_label  = np.zeros((trainLabel.shape[0], len(clfs)))\n\nfor j, clf in enumerate(clfs):\n    '''依次训练各个单模型'''\n    for i,(train, test) in enumerate(kf.split(trainLabel)):\n        '''使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。'''\n        # print(\"Fold\", i)\n        X_train, y_train, X_test, y_test = trainData[train], trainLabel[train], trainData[test], trainLabel[test]\n        clf.fit(X_train, y_train)\n        y_submission = clf.predict_proba(X_test)[:, 1]\n\n        # j 表示每一次的算法，而 test是交叉验证得到的每一行（也就是每一个算法把测试机和都预测了一遍）\n        dataset_stacking_train[test, j] = y_submission\n    \n# 用建立第二层模型\nmodel = LogisticRegression(C=0.1, max_iter=100)\nmodel.fit(dataset_stacking_train, trainLabel)\n\nscores = cross_val_score(model, dataset_stacking_train, trainLabel, cv=5, scoring='roc_auc')\nprint(scores.mean(), \"\\n\", scores)\n\n# 5. Blending\n# 0.8838950287185581 [0.87584416 0.91064935 0.89714286 0.85294118 0.8828976 ]\nclfs = [\n    AdaBoostClassifier(),\n    SVC(probability=True),\n    AdaBoostClassifier(),\n    LogisticRegression(C=0.1,max_iter=100),\n    XGBClassifier(max_depth=6,n_estimators=100,num_round = 5),\n    RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True),\n    GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)\n]\nX_d1, X_d2, y_d1, y_d2 = train_test_split(trainData, trainLabel, test_size=0.5, random_state=2017)\ndataset_d1 = np.zeros((X_d2.shape[0], len(clfs)))\ndataset_d2 = np.zeros((trainLabel.shape[0], len(clfs)))\n\nfor j, clf in enumerate(clfs):\n    #依次训练各个单模型\n    # 对于测试集，直接用这k个模型的预测值作为新的特征。\n    clf.fit(X_d1, y_d1)\n    dataset_d1[:, j] = clf.predict_proba(X_d2)[:, 1]\n\n# 用建立第二层模型\nmodel = LogisticRegression(C=0.1, max_iter=100)\nmodel.fit(dataset_d1, y_d2)\n\nscores = cross_val_score(model, dataset_d1, y_d2, cv=5, scoring='roc_auc')\nprint(scores.mean(), \"\\n\", scores)\n```\n\n## 五. 参数优化\n\n```py\nfrom sklearn.model_selection import GridSearchCV\nparam_test = {\n    # 'n_estimators': np.arange(190, 240, 2), \n    # 'max_depth': np.arange(4, 7, 1), \n    # 'learning_rate': np.array([0.01, 0.03, 0.05, 0.08, 0.1, 0.15, 0.2]), \n\n    'n_estimators': np.array([196]), \n    'max_depth': np.array([4]),     \n    'learning_rate': np.array([0.01, 0.02, 0.03, 0.04, 0.05]), \n    # 'min_child_weight': np.arange(1, 6, 2), \n    # 'C': (1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9)\n}\n\n# 0.8294499549079417   [0.82122905 0.80446927 0.86516854 0.82022472 0.83615819]\nmodel = XGBClassifier()\ngrid_search = GridSearchCV(estimator=model, param_grid=param_test, scoring='roc_auc', cv=5)\ngrid_search.fit(trainData, trainLabel)\nprint(\"最优得分 >>>\", grid_search.best_score_)\nprint(\"最优参数 >>>\", grid_search.best_params_)\n\n# 0.8685305085155506  [0.85770751 0.82002635 0.89632353 0.87018717 0.89840799]\nmodel = XGBClassifier(n_estimators=196, max_depth=4, learning_rate=0.03)\n\nscores = cross_val_score(model, trainData, trainLabel, cv=5, scoring='roc_auc')\nprint(scores.mean(), \"\\n\", scores)\n```\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/titanic/kaggle泰坦尼克之灾-风风组.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# kaggle泰坦尼克之灾\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {},\n   \"source\": [\n    \"步骤:\\n\",\n    \"一.导入数据包与数据集\\n\",\n    \"\\n\",\n    \"二.数据分析\\n\",\n    \"1.总体预览:了解每列数据的含义,数据的格式等\\n\",\n    \"2..数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备\\n\",\n    \"\\n\",\n    \"三.特征工程\\n\",\n    \"1.根据业务,常识,以及第二步的数据分析构造特征工程.\\n\",\n    \"2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)\\n\",\n    \"\\n\",\n    \"四.模型选择\\n\",\n    \"1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.\\n\",\n    \"2.比较各个模型的分数,然后取效果较好的模型作为基础模型.\\n\",\n    \"\\n\",\n    \"五.修改特征和模型参数\\n\",\n    \"1.可以通过添加或者修改特征,提高模型的上限.\\n\",\n    \"2.通过修改模型的参数,是模型逼近上限\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 一.导入数据包与数据集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 导入相关数据包\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"root_path = '/opt/git/kaggle/datasets/getting-started/titanic/input'\\n\",\n    \"\\n\",\n    \"train = pd.read_csv('%s/%s' % (root_path, 'train.csv'))\\n\",\n    \"test = pd.read_csv('%s/%s' % (root_path, 'test.csv'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 二.数据分析\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.总体预览\\n\",\n    \"了解每列数据的含义,数据的格式等\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Braund, Mr. Owen Harris</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>22.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>38.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C85</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Heikkinen, Miss. Laina</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>26.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>C123</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Allen, Mr. William Henry</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  \\\\\\n\",\n       \"0            1         0       3   \\n\",\n       \"1            2         1       1   \\n\",\n       \"2            3         1       3   \\n\",\n       \"3            4         1       1   \\n\",\n       \"4            5         0       3   \\n\",\n       \"\\n\",\n       \"                                                Name     Sex   Age  SibSp  \\\\\\n\",\n       \"0                            Braund, Mr. Owen Harris    male  22.0      1   \\n\",\n       \"1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \\n\",\n       \"2                             Heikkinen, Miss. Laina  female  26.0      0   \\n\",\n       \"3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \\n\",\n       \"4                           Allen, Mr. William Henry    male  35.0      0   \\n\",\n       \"\\n\",\n       \"   Parch            Ticket     Fare Cabin Embarked  \\n\",\n       \"0      0         A/5 21171   7.2500   NaN        S  \\n\",\n       \"1      0          PC 17599  71.2833   C85        C  \\n\",\n       \"2      0  STON/O2. 3101282   7.9250   NaN        S  \\n\",\n       \"3      0            113803  53.1000  C123        S  \\n\",\n       \"4      0            373450   8.0500   NaN        S  \"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head(5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"RangeIndex: 891 entries, 0 to 890\\n\",\n      \"Data columns (total 12 columns):\\n\",\n      \"PassengerId    891 non-null int64\\n\",\n      \"Survived       891 non-null int64\\n\",\n      \"Pclass         891 non-null int64\\n\",\n      \"Name           891 non-null object\\n\",\n      \"Sex            891 non-null object\\n\",\n      \"Age            714 non-null float64\\n\",\n      \"SibSp          891 non-null int64\\n\",\n      \"Parch          891 non-null int64\\n\",\n      \"Ticket         891 non-null object\\n\",\n      \"Fare           891 non-null float64\\n\",\n      \"Cabin          204 non-null object\\n\",\n      \"Embarked       889 non-null object\\n\",\n      \"dtypes: float64(2), int64(5), object(5)\\n\",\n      \"memory usage: 83.6+ KB\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 返回每列列名,该列非nan值个数,以及该列类型\\n\",\n    \"train.info()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"RangeIndex: 418 entries, 0 to 417\\n\",\n      \"Data columns (total 11 columns):\\n\",\n      \"PassengerId    418 non-null int64\\n\",\n      \"Pclass         418 non-null int64\\n\",\n      \"Name           418 non-null object\\n\",\n      \"Sex            418 non-null object\\n\",\n      \"Age            332 non-null float64\\n\",\n      \"SibSp          418 non-null int64\\n\",\n      \"Parch          418 non-null int64\\n\",\n      \"Ticket         418 non-null object\\n\",\n      \"Fare           417 non-null float64\\n\",\n      \"Cabin          91 non-null object\\n\",\n      \"Embarked       418 non-null object\\n\",\n      \"dtypes: float64(2), int64(4), object(5)\\n\",\n      \"memory usage: 36.0+ KB\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test.info()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"      <td>714.000000</td>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <td>446.000000</td>\\n\",\n       \"      <td>0.383838</td>\\n\",\n       \"      <td>2.308642</td>\\n\",\n       \"      <td>29.699118</td>\\n\",\n       \"      <td>0.523008</td>\\n\",\n       \"      <td>0.381594</td>\\n\",\n       \"      <td>32.204208</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>std</th>\\n\",\n       \"      <td>257.353842</td>\\n\",\n       \"      <td>0.486592</td>\\n\",\n       \"      <td>0.836071</td>\\n\",\n       \"      <td>14.526497</td>\\n\",\n       \"      <td>1.102743</td>\\n\",\n       \"      <td>0.806057</td>\\n\",\n       \"      <td>49.693429</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.420000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25%</th>\\n\",\n       \"      <td>223.500000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>2.000000</td>\\n\",\n       \"      <td>20.125000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>7.910400</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>50%</th>\\n\",\n       \"      <td>446.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>3.000000</td>\\n\",\n       \"      <td>28.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>14.454200</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>75%</th>\\n\",\n       \"      <td>668.500000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>3.000000</td>\\n\",\n       \"      <td>38.000000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>31.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>3.000000</td>\\n\",\n       \"      <td>80.000000</td>\\n\",\n       \"      <td>8.000000</td>\\n\",\n       \"      <td>6.000000</td>\\n\",\n       \"      <td>512.329200</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       PassengerId    Survived      Pclass         Age       SibSp  \\\\\\n\",\n       \"count   891.000000  891.000000  891.000000  714.000000  891.000000   \\n\",\n       \"mean    446.000000    0.383838    2.308642   29.699118    0.523008   \\n\",\n       \"std     257.353842    0.486592    0.836071   14.526497    1.102743   \\n\",\n       \"min       1.000000    0.000000    1.000000    0.420000    0.000000   \\n\",\n       \"25%     223.500000    0.000000    2.000000   20.125000    0.000000   \\n\",\n       \"50%     446.000000    0.000000    3.000000   28.000000    0.000000   \\n\",\n       \"75%     668.500000    1.000000    3.000000   38.000000    1.000000   \\n\",\n       \"max     891.000000    1.000000    3.000000   80.000000    8.000000   \\n\",\n       \"\\n\",\n       \"            Parch        Fare  \\n\",\n       \"count  891.000000  891.000000  \\n\",\n       \"mean     0.381594   32.204208  \\n\",\n       \"std      0.806057   49.693429  \\n\",\n       \"min      0.000000    0.000000  \\n\",\n       \"25%      0.000000    7.910400  \\n\",\n       \"50%      0.000000   14.454200  \\n\",\n       \"75%      0.000000   31.000000  \\n\",\n       \"max      6.000000  512.329200  \"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 返回数值型变量的统计量\\n\",\n    \"# train.describe(percentiles=[0.00, 0.25, 0.5, 0.75, 1.00])\\n\",\n    \"train.describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2..数据初步分析,使用统计学与绘图\\n\",\n    \"目的:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0    549\\n\",\n       \"1    342\\n\",\n       \"Name: Survived, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 存活人数\\n\",\n    \"train['Survived'].value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1)数值型数据协方差,corr()函数\\n\",\n    \"来个总览,快速了解个数据的相关性\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>-0.338481</td>\\n\",\n       \"      <td>-0.077221</td>\\n\",\n       \"      <td>-0.035322</td>\\n\",\n       \"      <td>0.081629</td>\\n\",\n       \"      <td>0.257307</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <td>-0.338481</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>-0.369226</td>\\n\",\n       \"      <td>0.083081</td>\\n\",\n       \"      <td>0.018443</td>\\n\",\n       \"      <td>-0.549500</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <td>-0.077221</td>\\n\",\n       \"      <td>-0.369226</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>-0.308247</td>\\n\",\n       \"      <td>-0.189119</td>\\n\",\n       \"      <td>0.096067</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <td>-0.035322</td>\\n\",\n       \"      <td>0.083081</td>\\n\",\n       \"      <td>-0.308247</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.414838</td>\\n\",\n       \"      <td>0.159651</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <td>0.081629</td>\\n\",\n       \"      <td>0.018443</td>\\n\",\n       \"      <td>-0.189119</td>\\n\",\n       \"      <td>0.414838</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.216225</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <td>0.257307</td>\\n\",\n       \"      <td>-0.549500</td>\\n\",\n       \"      <td>0.096067</td>\\n\",\n       \"      <td>0.159651</td>\\n\",\n       \"      <td>0.216225</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"          Survived    Pclass       Age     SibSp     Parch      Fare\\n\",\n       \"Survived  1.000000 -0.338481 -0.077221 -0.035322  0.081629  0.257307\\n\",\n       \"Pclass   -0.338481  1.000000 -0.369226  0.083081  0.018443 -0.549500\\n\",\n       \"Age      -0.077221 -0.369226  1.000000 -0.308247 -0.189119  0.096067\\n\",\n       \"SibSp    -0.035322  0.083081 -0.308247  1.000000  0.414838  0.159651\\n\",\n       \"Parch     0.081629  0.018443 -0.189119  0.414838  1.000000  0.216225\\n\",\n       \"Fare      0.257307 -0.549500  0.096067  0.159651  0.216225  1.000000\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 相关性协方差表,corr()函数,返回结果接近0说明无相关性,大于0说明是正相关,小于0是负相关.\\n\",\n    \"train_corr = train.drop('PassengerId',axis=1).corr()\\n\",\n    \"train_corr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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PNT6fZpxpjKK3eKiIiI1EPGmOHGmA3GmM3GmLur2H+CMeYLY8waY8w6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\\n+Q2QTU4NAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xbde7438>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#画出相关性热力图\\n\",\n    \"a = plt.subplots(figsize=(15,9))#调整画布大小\\n\",\n    \"a = sns.heatmap(train_corr, vmin=-1, vmax=1 , annot=True , square=True)#画热力图\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2)各个数据与结果的关系\\n\",\n    \"进一步探索分析各个数据与结果的关系\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### ①Pclass,乘客等级,1是最高级\\n\",\n    \"结果分析:可以看出Survived和Pclass在Pclass=1的时候有较强的相关性（>0.5），所以最终模型中包含该特征。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.629630</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.472826</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>0.242363</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"        Pclass  Survived\\n\",\n       \"Pclass                  \\n\",\n       \"1          1.0  0.629630\\n\",\n       \"2          2.0  0.472826\\n\",\n       \"3          3.0  0.242363\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.groupby(['Pclass'])['Pclass','Survived'].mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0xc33fa90>\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXcAAAEGCAYAAACevtWaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xc8761d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"train[['Pclass','Survived']].groupby(['Pclass']).mean().plot.bar()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### ②Sex,性别\\n\",\n    \"结果分析:女性有更高的活下来的概率（74%）,保留该特征\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>female</th>\\n\",\n       \"      <td>0.742038</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>male</th>\\n\",\n       \"      <td>0.188908</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"        Survived\\n\",\n       \"Sex             \\n\",\n       \"female  0.742038\\n\",\n       \"male    0.188908\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.groupby(['Sex'])['Sex','Survived'].mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x105c4b630>\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1149c59e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"train[['Sex','Survived']].groupby(['Sex']).mean().plot.bar()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### ③SibSp and Parch  兄妹配偶数/父母子女数\\n\",\n    \"结果分析:这些特征与特定的值没有相关性不明显，最好是由这些独立的特征派生出一个新特征或者一组新特征\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0.345395</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0.535885</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0.464286</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0.166667</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       Survived\\n\",\n       \"SibSp          \\n\",\n       \"0      0.345395\\n\",\n       \"1      0.535885\\n\",\n       \"2      0.464286\\n\",\n       \"3      0.250000\\n\",\n       \"4      0.166667\\n\",\n       \"5      0.000000\\n\",\n       \"8      0.000000\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train[['SibSp','Survived']].groupby(['SibSp']).mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"-\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1144385c0>\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x113cc3eb8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"train[['Parch','Survived']].groupby(['Parch']).mean().plot.bar()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### ④Age年龄与生存情况的分析.\\n\",\n    \"结果分析:由图,可以看到年龄是影响生存情况的. \\n\",\n    \"\\n\",\n    \"但是年龄是有大部分缺失值的,缺失值需要进行处理,可以使用填充或者模型预测.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0xc5f7cf8>\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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4Dc3b+b5DMnLX5+kiun21cmecGS6wJgoBcDrM5O50F+XHffmSTdfWdV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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xc5f76d8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"g = sns.FacetGrid(train, col='Survived',size=5)\\n\",\n    \"g.map(plt.hist, 'Age', bins=40)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0xc71ac18>\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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EPD0NCronTjxNKNERYG1+NoHOrs/uFhbasDS9dweqFRyutVesNUtNS4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\\nRK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xc7a7a58>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"train.groupby(['Age'])['Survived'].mean().plot()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### ⑤Embarked登港港口与生存情况的分析\\n\",\n    \"结果分析:C地的生存率更高,这个也应该保留为模型特征.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0xca1e5f8>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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jMzqxilHoP4fN6F2J5euWFkuUtotyHXPlvuEsysg5T6wKBqSfdJel3S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\\negAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x59b99e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.countplot('Embarked',hue='Survived',data=train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {},\n   \"source\": [\n    \"⑥其他因素\\n\",\n    \"*在数据的Name项中包含了对该乘客的称呼，如Mr、Miss等，这些信息包含了乘客的年龄、性别、也有可能包含社会地位，如Dr、Lady、Major、Master等称呼。这一项不方便用图表展示，但是在特征工程中，我们会将其提取出来,然后放到模型中。\\n\",\n    \"\\n\",\n    \"*剩余因素还有船票价格、船舱号和船票号，这三个因素都可能会影响乘客在船中的位置从而影响逃生顺序，但是因为这三个因素与生存之间看不出明显规律，所以在后期模型融合时，将这些因素交给模型来决定其重要性。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 三.特征工程\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#先将数据集合并,一起做特征工程(注意,标准化的时候需要分开处理)\\n\",\n    \"#先将test补齐,然后通过pd.apped()合并\\n\",\n    \"test['Survived'] = 0\\n\",\n    \"train_test = train.append(test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### ①Pclass,乘客等级,1是最高级\\n\",\n    \"两种方式:一是该特征不做处理,可以直接保留.二是再处理:也进行分列处理(比较那种方式模型效果更好,就选那种)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_test = pd.get_dummies(train_test,columns=['Pclass'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ②Sex,性别¶\\n\",\n    \"无缺失值,直接分列\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_test = pd.get_dummies(train_test,columns=[\\\"Sex\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ③SibSp and Parch  兄妹配偶数/父母子女数\\n\",\n    \"第一次直接保留:这两个都影响生存率,且都是数值型,先直接保存.\\n\",\n    \"\\n\",\n    \"第二次进行两项求和,并进行分列处理.(兄妹配偶数和父母子女数都是认识人的数量,所以总数可能也会更好)(模型结果提高到了)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#这是剑豪模型后回来添加的新特征,模型的分数最终有所提高了.\\n\",\n    \"train_test['SibSp_Parch'] = train_test['SibSp'] + train_test['Parch']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_test = pd.get_dummies(train_test,columns = ['SibSp','Parch','SibSp_Parch']) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ④Embarked \\n\",\n    \"数据有极少量(3个)缺失值,但是在分列的时候,缺失值的所有列可以均为0,所以可以考虑不填充.\\n\",\n    \"\\n\",\n    \"另外,也可以考虑用测试集众数来填充.先找出众数,再采用df.fillna()方法\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_test = pd.get_dummies(train_test,columns=[\\\"Embarked\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### ⑤ Name\\n\",\n    \"1.在数据的Name项中包含了对该乘客的称呼,将这些关键词提取出来,然后做分列处理.(参考别人的)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)\\n\",\n      \"  \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#从名字中提取出称呼： df['Name].str.extract()是提取函数,配合正则一起使用\\n\",\n    \"train_test['Name1'] = train_test['Name'].str.extract('.+,(.+)').str.extract( '^(.+?)\\\\.').str.strip()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#将姓名分类处理()\\n\",\n    \"train_test['Name1'].replace(['Capt', 'Col', 'Major', 'Dr', 'Rev'], 'Officer' , inplace = True)\\n\",\n    \"train_test['Name1'].replace(['Jonkheer', 'Don', 'Sir', 'the Countess', 'Dona', 'Lady'], 'Royalty' , inplace = True)\\n\",\n    \"train_test['Name1'].replace(['Mme', 'Ms', 'Mrs'], 'Mrs')\\n\",\n    \"train_test['Name1'].replace(['Mlle', 'Miss'], 'Miss')\\n\",\n    \"train_test['Name1'].replace(['Mr'], 'Mr' , inplace = True)\\n\",\n    \"train_test['Name1'].replace(['Master'], 'Master' , inplace = True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#分列处理\\n\",\n    \"train_test = pd.get_dummies(train_test,columns=['Name1'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### 从姓名中提取出姓做特征\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#从姓名中提取出姓\\n\",\n    \"train_test['Name2'] = train_test['Name'].apply(lambda x: x.split('.')[1])\\n\",\n    \"\\n\",\n    \"#计算数量,然后合并数据集\\n\",\n    \"Name2_sum = train_test['Name2'].value_counts().reset_index()\\n\",\n    \"Name2_sum.columns=['Name2','Name2_sum']\\n\",\n    \"train_test = pd.merge(train_test,Name2_sum,how='left',on='Name2')\\n\",\n    \"\\n\",\n    \"#由于出现一次时该特征时无效特征,用one来代替出现一次的姓\\n\",\n    \"train_test.loc[train_test['Name2_sum'] == 1 , 'Name2_new'] = 'one'\\n\",\n    \"train_test.loc[train_test['Name2_sum'] > 1 , 'Name2_new'] = train_test['Name2']\\n\",\n    \"del train_test['Name2']\\n\",\n    \"\\n\",\n    \"#分列处理\\n\",\n    \"train_test = pd.get_dummies(train_test,columns=['Name2_new'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#删掉姓名这个特征\\n\",\n    \"del train_test['Name']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ⑥ Fare\\n\",\n    \"该特征有缺失值,先找出缺失值的那调数据,然后用平均数填充\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Pclass_1</th>\\n\",\n       \"      <th>Pclass_2</th>\\n\",\n       \"      <th>Pclass_3</th>\\n\",\n       \"      <th>Sex_female</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>Name2_new_ Thomas Henry</th>\\n\",\n       \"      <th>Name2_new_ Victor</th>\\n\",\n       \"      <th>Name2_new_ Washington</th>\\n\",\n       \"      <th>Name2_new_ William</th>\\n\",\n       \"      <th>Name2_new_ William Edward</th>\\n\",\n       \"      <th>Name2_new_ William Henry</th>\\n\",\n       \"      <th>Name2_new_ William James</th>\\n\",\n       \"      <th>Name2_new_ William John</th>\\n\",\n       \"      <th>Name2_new_ William Thomas</th>\\n\",\n       \"      <th>Name2_new_one</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1043</th>\\n\",\n       \"      <td>60.5</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1044</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3701</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>1 rows × 137 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       Age Cabin  Fare  PassengerId  Survived Ticket  Pclass_1  Pclass_2  \\\\\\n\",\n       \"1043  60.5   NaN   NaN         1044         0   3701         0         0   \\n\",\n       \"\\n\",\n       \"      Pclass_3  Sex_female      ...        Name2_new_ Thomas Henry  \\\\\\n\",\n       \"1043         1           0      ...                              0   \\n\",\n       \"\\n\",\n       \"      Name2_new_ Victor  Name2_new_ Washington  Name2_new_ William  \\\\\\n\",\n       \"1043                  0                      0                   0   \\n\",\n       \"\\n\",\n       \"      Name2_new_ William Edward  Name2_new_ William Henry  \\\\\\n\",\n       \"1043                          0                         0   \\n\",\n       \"\\n\",\n       \"      Name2_new_ William James  Name2_new_ William John  \\\\\\n\",\n       \"1043                         0                        0   \\n\",\n       \"\\n\",\n       \"      Name2_new_ William Thomas  Name2_new_one  \\n\",\n       \"1043                          0              0  \\n\",\n       \"\\n\",\n       \"[1 rows x 137 columns]\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"#从上面的分析,发现该特征train集无miss值,test有一个缺失值,先查看\\n\",\n    \"train_test.loc[train_test[\\\"Fare\\\"].isnull()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Pclass  Embarked\\n\",\n       \"1       C           104.718529\\n\",\n       \"        Q            90.000000\\n\",\n       \"        S            70.364862\\n\",\n       \"2       C            25.358335\\n\",\n       \"        Q            12.350000\\n\",\n       \"        S            20.327439\\n\",\n       \"3       C            11.214083\\n\",\n       \"        Q            11.183393\\n\",\n       \"        S            14.644083\\n\",\n       \"Name: Fare, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"#票价与pclass和Embarked有关,所以用train分组后的平均数填充\\n\",\n    \"train.groupby(by=[\\\"Pclass\\\",\\\"Embarked\\\"]).Fare.mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#用pclass=3和Embarked=S的平均数14.644083来填充\\n\",\n    \"train_test[\\\"Fare\\\"].fillna(14.435422,inplace=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ⑦ Ticket\\n\",\n    \"该列和名字做类似的处理,先提取,然后分列\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#将Ticket提取字符列\\n\",\n    \"#str.isnumeric()  如果S中只有数字字符，则返回True，否则返回False\\n\",\n    \"train_test['Ticket_Letter'] = train_test['Ticket'].str.split().str[0]\\n\",\n    \"train_test['Ticket_Letter'] = train_test['Ticket_Letter'].apply(lambda x:np.nan if x.isnumeric() else x)\\n\",\n    \"train_test.drop('Ticket',inplace=True,axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#分列,此时nan值可以不做处理\\n\",\n    \"train_test = pd.get_dummies(train_test,columns=['Ticket_Letter'],drop_first=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### ⑧ Age\\n\",\n    \"1.该列有大量缺失值,考虑用一个回归模型进行填充.\\n\",\n    \"\\n\",\n    \"2.在模型修改的时候,考虑到年龄缺失值可能影响死亡情况,用年龄是否缺失值来构造新特征\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.19771863117870722\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"这是模型就好后回来增加的新特征\\n\",\n    \"考虑年龄缺失值可能影响死亡情况,数据表明,年龄缺失的死亡率为0.19.\\\"\\\"\\\"\\n\",\n    \"train_test.loc[train_test[\\\"Age\\\"].isnull()]['Survived'].mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 所以用年龄是否缺失值来构造新特征\\n\",\n    \"train_test.loc[train_test[\\\"Age\\\"].isnull() ,\\\"age_nan\\\"] = 1\\n\",\n    \"train_test.loc[train_test[\\\"Age\\\"].notnull() ,\\\"age_nan\\\"] = 0\\n\",\n    \"train_test = pd.get_dummies(train_test,columns=['age_nan'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 利用其他组特征量，采用机器学习算法来预测Age\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"Int64Index: 1309 entries, 0 to 1308\\n\",\n      \"Columns: 187 entries, Age to age_nan_1.0\\n\",\n      \"dtypes: float64(2), int64(3), object(1), uint8(181)\\n\",\n      \"memory usage: 343.0+ KB\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_test.info()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#创建没有['Age','Survived']的数据集\\n\",\n    \"missing_age = train_test.drop(['Survived','Cabin'],axis=1)\\n\",\n    \"#将Age完整的项作为训练集、将Age缺失的项作为测试集。\\n\",\n    \"missing_age_train = missing_age[missing_age['Age'].notnull()]\\n\",\n    \"missing_age_test = missing_age[missing_age['Age'].isnull()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#构建训练集合预测集的X和Y值\\n\",\n    \"missing_age_X_train = missing_age_train.drop(['Age'], axis=1)\\n\",\n    \"missing_age_Y_train = missing_age_train['Age']\\n\",\n    \"missing_age_X_test = missing_age_test.drop(['Age'], axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 先将数据标准化\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"ss = StandardScaler()\\n\",\n    \"#用测试集训练并标准化\\n\",\n    \"ss.fit(missing_age_X_train)\\n\",\n    \"missing_age_X_train = ss.transform(missing_age_X_train)\\n\",\n    \"missing_age_X_test = ss.transform(missing_age_X_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#使用贝叶斯预测年龄\\n\",\n    \"from sklearn import linear_model\\n\",\n    \"lin = linear_model.BayesianRidge()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True,\\n\",\n       \"       fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300,\\n\",\n       \"       normalize=False, tol=0.001, verbose=False)\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"lin.fit(missing_age_X_train,missing_age_Y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#利用loc将预测值填入数据集\\n\",\n    \"train_test.loc[(train_test['Age'].isnull()), 'Age'] = lin.predict(missing_age_X_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#将年龄划分是个阶段10以下,10-18,18-30,30-50,50以上\\n\",\n    \"train_test['Age'] = pd.cut(train_test['Age'], bins=[0,10,18,30,50,100],labels=[1,2,3,4,5])\\n\",\n    \"\\n\",\n    \"train_test = pd.get_dummies(train_test,columns=['Age'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ⑨ Cabin\\n\",\n    \"cabin项缺失太多，只能将有无Cain首字母进行分类,缺失值为一类,作为特征值进行建模,也可以考虑直接舍去该特征\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#cabin项缺失太多，只能将有无Cain首字母进行分类,缺失值为一类,作为特征值进行建模\\n\",\n    \"train_test['Cabin'] = train_test['Cabin'].apply(lambda x:str(x)[0] if pd.notnull(x) else x)\\n\",\n    \"train_test = pd.get_dummies(train_test,columns=['Cabin'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#cabin项缺失太多，只能将有无Cain首字母进行分类,\\n\",\n    \"train_test.loc[train_test[\\\"Cabin\\\"].isnull() ,\\\"Cabin_nan\\\"] = 1\\n\",\n    \"train_test.loc[train_test[\\\"Cabin\\\"].notnull() ,\\\"Cabin_nan\\\"] = 0\\n\",\n    \"train_test = pd.get_dummies(train_test,columns=['Cabin_nan'])\\n\",\n    \"train_test.drop('Cabin',axis=1,inplace=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ⑩ 特征工程处理完了,划分数据集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_data = train_test[:891]\\n\",\n    \"test_data = train_test[891:]\\n\",\n    \"train_data_X = train_data.drop(['Survived'],axis=1)\\n\",\n    \"train_data_Y = train_data['Survived']\\n\",\n    \"test_data_X = test_data.drop(['Survived'],axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 四 建立模型\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 数据标准化\\n\",\n    \"1.线性模型需要用标准化的数据建模,而树类模型不需要标准化的数据\\n\",\n    \"\\n\",\n    \"2.处理标准化的时候,注意将测试集的数据transform到test集上\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"ss2 = StandardScaler()\\n\",\n    \"ss2.fit(train_data_X)\\n\",\n    \"train_data_X_sd = ss2.transform(train_data_X)\\n\",\n    \"test_data_X_sd = ss2.transform(test_data_X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 开始建模\\n\",\n    \"1.可选单个模型模型有随机森林,逻辑回归,svm,xgboost,gbdt等.\\n\",\n    \"\\n\",\n    \"2.也可以将多个模型组合起来,进行模型融合,比如voting,stacking等方法\\n\",\n    \"\\n\",\n    \"3.好的特征决定模型上限,好的模型和参数可以无线逼近上限.\\n\",\n    \"\\n\",\n    \"4.我测试了多种模型,模型结果最高的随机森林,最高有0.8.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 随机森林\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"\\n\",\n    \"rf = RandomForestClassifier(n_estimators=150,min_samples_leaf=3,max_depth=6,oob_score=True)\\n\",\n    \"rf.fit(train_data_X,train_data_Y)\\n\",\n    \"\\n\",\n    \"test[\\\"Survived\\\"] = rf.predict(test_data_X)\\n\",\n    \"RF = test[['PassengerId','Survived']].set_index('PassengerId')\\n\",\n    \"RF.to_csv('RF.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {},\n   \"source\": [\n    \"#随机森林是随机选取特征进行建模的,所以每次的结果可能都有点小差异\\n\",\n    \"#如果分数足够好,可以将该模型保存起来,下次直接调出来使用0.81339 'rf10.pkl'\\n\",\n    \"from sklearn.externals import joblib\\n\",\n    \"joblib.dump(rf, 'rf10.pkl')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### LogisticRegression\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"\\n\",\n    \"lr = LogisticRegression()\\n\",\n    \"\\n\",\n    \"from sklearn.grid_search import GridSearchCV\\n\",\n    \"\\n\",\n    \"param = {'C':[0.001,0.01,0.1,1,10],\\\"max_iter\\\":[100,250]}\\n\",\n    \"\\n\",\n    \"clf = GridSearchCV(lr,param,cv=5,n_jobs=-1,verbose=1,scoring=\\\"roc_auc\\\")\\n\",\n    \"\\n\",\n    \"clf.fit(train_data_X_sd,train_data_Y)\\n\",\n    \"\\n\",\n    \"#打印参数的得分情况\\n\",\n    \"clf.grid_scores_\\n\",\n    \"\\n\",\n    \"#打印最佳参数\\n\",\n    \"clf.best_params_\\n\",\n    \"\\n\",\n    \"#将最佳参数传入训练模型\\n\",\n    \"lr = LogisticRegression(clf.best_params_)\\n\",\n    \"\\n\",\n    \"lr.fit(train_data_X_sd,train_data_Y)\\n\",\n    \"\\n\",\n    \"#预测结果\\n\",\n    \"lr.predict(test_data_X_sd)\\n\",\n    \"\\n\",\n    \"#打印结果\\n\",\n    \"test[\\\"Survived\\\"] = lr.predict(test_data_X_sd)\\n\",\n    \"LS = test[['PassengerId','Survived']].set_index('PassengerId')\\n\",\n    \"\\n\",\n    \"#输出结果\\n\",\n    \"LS.to_csv('LS5.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### SVM\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {},\n   \"source\": [\n    \"from sklearn import svm\\n\",\n    \"svc = svm.SVC()\\n\",\n    \"\\n\",\n    \"clf = GridSearchCV(svc,param,cv=5,n_jobs=-1,verbose=1,scoring=\\\"roc_auc\\\")\\n\",\n    \"clf.fit(train_data_X_sd,train_data_Y)\\n\",\n    \"\\n\",\n    \"clf.best_params_\\n\",\n    \"\\n\",\n    \"svc = svm.SVC(C=1,max_iter=250)\\n\",\n    \"\\n\",\n    \"#训练模型并预测结果\\n\",\n    \"svc.fit(train_data_X_sd,train_data_Y)\\n\",\n    \"svc.predict(test_data_X_sd)\\n\",\n    \"\\n\",\n    \"#打印结果\\n\",\n    \"test[\\\"Survived\\\"] = svc.predict(test_data_X_sd)\\n\",\n    \"SVM = test[['PassengerId','Survived']].set_index('PassengerId')\\n\",\n    \"SVM.to_csv('svm1.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### xgboost\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"import xgboost as xgb\\n\",\n    \"\\n\",\n    \"xgb_model = xgb.XGBClassifier(n_estimators=150,min_samples_leaf=3,max_depth=6)\\n\",\n    \"\\n\",\n    \"xgb_model.fit(train_data_X,train_data_Y)\\n\",\n    \"\\n\",\n    \"test[\\\"Survived\\\"] = xgb_model.predict(test_data_X)\\n\",\n    \"XGB = test[['PassengerId','Survived']].set_index('PassengerId')\\n\",\n    \"XGB.to_csv('XGB5.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### GBDT\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"from sklearn.ensemble import GradientBoostingClassifier\\n\",\n    \"\\n\",\n    \"gbdt = GradientBoostingClassifier(learning_rate=0.7,max_depth=6,n_estimators=100,min_samples_leaf=2)\\n\",\n    \"\\n\",\n    \"gbdt.fit(train_data_X,train_data_Y)\\n\",\n    \"\\n\",\n    \"test[\\\"Survived\\\"] = gbdt.predict(test_data_X)\\n\",\n    \"test[['PassengerId','Survived']].set_index('PassengerId').to_csv('gbdt3.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### 模型融合voting\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"from sklearn.ensemble import VotingClassifier\\n\",\n    \"\\n\",\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"lr = LogisticRegression(C=0.1,max_iter=100)\\n\",\n    \"\\n\",\n    \"import xgboost as xgb\\n\",\n    \"xgb_model = xgb.XGBClassifier(max_depth=6,min_samples_leaf=2,n_estimators=100,num_round = 5)\\n\",\n    \"\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"rf = RandomForestClassifier(n_estimators=200,min_samples_leaf=2,max_depth=6,oob_score=True)\\n\",\n    \"\\n\",\n    \"from sklearn.ensemble import GradientBoostingClassifier\\n\",\n    \"gbdt = GradientBoostingClassifier(learning_rate=0.1,min_samples_leaf=2,max_depth=6,n_estimators=100)\\n\",\n    \"\\n\",\n    \"vot = VotingClassifier(estimators=[('lr', lr), ('rf', rf),('gbdt',gbdt),('xgb',xgb_model)], voting='hard')\\n\",\n    \"\\n\",\n    \"vot.fit(train_data_X_sd,train_data_Y)\\n\",\n    \"\\n\",\n    \"vot.predict(test_data_X_sd)\\n\",\n    \"\\n\",\n    \"test[\\\"Survived\\\"] = vot.predict(test_data_X_sd)\\n\",\n    \"test[['PassengerId','Survived']].set_index('PassengerId').to_csv('vot5.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 模型融合stacking\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {},\n   \"source\": [\n    \"#划分train数据集,调用代码,把数据集名字转成和代码一样\\n\",\n    \"X = train_data_X_sd\\n\",\n    \"X_predict = test_data_X_sd\\n\",\n    \"y = train_data_Y\\n\",\n    \"\\n\",\n    \"'''模型融合中使用到的各个单模型'''\\n\",\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"from sklearn import svm\\n\",\n    \"import xgboost as xgb\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"from sklearn.ensemble import GradientBoostingClassifier\\n\",\n    \"\\n\",\n    \"clfs = [LogisticRegression(C=0.1,max_iter=100),\\n\",\n    \"        xgb.XGBClassifier(max_depth=6,n_estimators=100,num_round = 5),\\n\",\n    \"        RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True),\\n\",\n    \"        GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)]\\n\",\n    \"\\n\",\n    \"#创建n_folds\\n\",\n    \"from sklearn.cross_validation import StratifiedKFold\\n\",\n    \"n_folds = 5\\n\",\n    \"skf = list(StratifiedKFold(y, n_folds))\\n\",\n    \"\\n\",\n    \"#创建零矩阵\\n\",\n    \"dataset_blend_train = np.zeros((X.shape[0], len(clfs)))\\n\",\n    \"dataset_blend_test = np.zeros((X_predict.shape[0], len(clfs)))\\n\",\n    \"\\n\",\n    \"#建立模型\\n\",\n    \"for j, clf in enumerate(clfs):\\n\",\n    \"    '''依次训练各个单模型'''\\n\",\n    \"    # print(j, clf)\\n\",\n    \"    dataset_blend_test_j = np.zeros((X_predict.shape[0], len(skf)))\\n\",\n    \"    for i, (train, test) in enumerate(skf):\\n\",\n    \"        '''使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。'''\\n\",\n    \"        # print(\\\"Fold\\\", i)\\n\",\n    \"        X_train, y_train, X_test, y_test = X[train], y[train], X[test], y[test]\\n\",\n    \"        clf.fit(X_train, y_train)\\n\",\n    \"        y_submission = clf.predict_proba(X_test)[:, 1]\\n\",\n    \"        dataset_blend_train[test, j] = y_submission\\n\",\n    \"        dataset_blend_test_j[:, i] = clf.predict_proba(X_predict)[:, 1]\\n\",\n    \"    '''对于测试集，直接用这k个模型的预测值均值作为新的特征。'''\\n\",\n    \"    dataset_blend_test[:, j] = dataset_blend_test_j.mean(1)\\n\",\n    \"\\n\",\n    \"# 用建立第二层模型\\n\",\n    \"clf2 = LogisticRegression(C=0.1,max_iter=100)\\n\",\n    \"clf2.fit(dataset_blend_train, y)\\n\",\n    \"y_submission = clf2.predict_proba(dataset_blend_test)[:, 1]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"test = pd.read_csv(\\\"test.csv\\\")\\n\",\n    \"test[\\\"Survived\\\"] = clf2.predict(dataset_blend_test)\\n\",\n    \"test[['PassengerId','Survived']].set_index('PassengerId').to_csv('stack3.csv')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/titanic/titanic-data-science-solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"ea25cdf7-bdbc-3cf1-0737-bc51675e3374\",\n    \"_uuid\": \"9e170e99b8fc43e1d5ed5075e05397f73c10dcc7\"\n   },\n   \"source\": [\n    \"# 《泰坦尼克号》数据科学解决方案\\n\",\n    \"\\n\",\n    \"## 翻译相关\\n\",\n    \"原文地址: <https://www.kaggle.com/startupsci/titanic-data-science-solutions?scriptVersionId=1145136>  \\n\",\n    \"开源组织: [ApacheCN](http://www.apachecn.org)  \\n\",\n    \"贡献者: [@那伊抹微笑](https://github.com/wangyangting), [@李铭哲](https://github.com/limingzhe), [@刘海飞](https://github.com/WindZQ), [@王德红](https://github.com/VPrincekin)  [@成飘飘](https://github.com/chengpiaopiao)  \\n\",\n    \"最近更新: 2018-01-03\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### 我已经发布了一个新的 Python 包 [Speedml](https://speedml.com), 它将该 notebook 中的使用的技术编译成一个 intuitive（直观的），powerful（功能强大的）且 productive（高效的）API.\\n\",\n    \"\\n\",\n    \"### Speedml 帮助我在 Kaggle 排行榜上从最低的 80% 跳到最高的 20%, 迭代的次数很少.\\n\",\n    \"\\n\",\n    \"### 还有一件事...Speedml 实现了这一点, 代码行数减少了近 70%!\\n\",\n    \"\\n\",\n    \"### 下载并且运行代码 [Speedml 版本的泰坦尼克号解决方案](https://github.com/Speedml/notebooks/blob/master/titanic/titanic-solution-using-speedml.ipynb).\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"该 notebook 是 [Data Science Solutions](https://startupsci.com) 书籍的一个手册. 该 notebook 引导我们通过一个典型的工作流程来解决像 Kaggle 这样类似的网站的数据科学竞赛.\\n\",\n    \"\\n\",\n    \"有几个优秀的 notebooks 可以用来研究数据科学竞赛作品.\\n\",\n    \"然而许多手册将会跳过一些关于如何开发解决方案的解释, 因为这些 notebooks 是专门为这些专家开发的.\\n\",\n    \"该 notebook 的目标是遵循一步一步的工作流程, 解释我们在解决方案开发过程中所做的每一个决策的每个步骤和理由.\\n\",\n    \"\\n\",\n    \"## 工作流阶段\\n\",\n    \"\\n\",\n    \"1. 问题或问题的定义.\\n\",\n    \"2. 获取 training（训练）和 testing（测试）数据.\\n\",\n    \"3. Wrangle（整理）, prepare（准备）, cleanse（清洗）数据\\n\",\n    \"4. Analyze（分析）, identify patterns 以及探索数据.\\n\",\n    \"5. Model（模型）, predict（预测）以及解决问题.\\n\",\n    \"6. Visualize（可视化）, report（报告）和提出解决问题的步骤以及最终解决方案.\\n\",\n    \"7. 提供或提交结果.\\n\",\n    \"\\n\",\n    \"该工作流指出了，每个阶段如何遵循另一个阶段的常见顺序.\\n\",\n    \"但是也有例外的场景.\\n\",\n    \"\\n\",\n    \"- 我们可能结合多个工作流阶段. 我们可以通过可视化数据进行分析.\\n\",\n    \"- 比 indicated（说明）更早的进行一个阶段. 我们可能在 wrangling（整理）过程的前后来分析数据.\\n\",\n    \"- 在我们的工作流程中多次执行一个阶段. 可视化阶段可能被使用多次.\\n\",\n    \"- Drop a stage altogether. We may not need supply stage to productize or service enable our dataset for a competition.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## 问题和问题定义\\n\",\n    \"\\n\",\n    \"像 Kaggle 这样的竞赛网站, 它们会定义要解决或质疑的问题, 同时提供用于训练数据科学模型和根据测试数据集测试模型结果的数据集,（即, 训练集 和 测试集）.\\n\",\n    \"针对《泰坦尼克号生存竞赛》的问题或定义在 [这里是 Kaggle 描述](https://www.kaggle.com/c/titanic) 中有描述.\\n\",\n    \"\\n\",\n    \"> 从泰坦尼克号的灾难中幸存下来或没有幸存的乘客的样本训练集（train.csv）中，如果测试数据集（test.csv）中的这些乘客幸存下来，我们的模型是否可以基于给定的测试数据集（test.csv）来确定。\\n\",\n    \"\\n\",\n    \"我们也可能希望对我们问题的领域有所了解.\\n\",\n    \"这在 [Kaggle 竞赛描述](https://www.kaggle.com/c/titanic) 页面有详细的描述.\\n\",\n    \"以下是要注意的事项.\\n\",\n    \"\\n\",\n    \"- 1912年4月15日, 在首航期间, 泰坦尼克号撞上一座冰山后沉没, 2224 名乘客和机组人员中有 1502 人遇难. 生成率解释为 32%.\\n\",\n    \"- 还难导致生命损失的原因之一是没有足够的救生艇给乘客和船员.\\n\",\n    \"- 尽管幸存下来的运气有一些因素, 但一些人比其他人更有可能幸存下来，比如妇女, 儿童和上层阶级.\\n\",\n    \"\\n\",\n    \"## 工作流目标\\n\",\n    \"\\n\",\n    \"数据科学解决方案工作流程有以下七个主要的目标.\\n\",\n    \"\\n\",\n    \"**Classifying（分类）.** 我们可能想对我们的样本进行分类或加以类别. 我们也可能想要了解不同类别与解决方案目标的含义或相关性.\\n\",\n    \"\\n\",\n    \"**Correlating（相关）.** 可以根据训练数据集中的可用特征来处理这个问题. 数据集中的哪些特征对我们的解决方案目标有重大贡献？从统计学上讲, 特征和解决方案的目标中有一个[相关](https://en.wikiversity.org/wiki/Correlation)？随着特征值的改变, 解决方案的状态也会随之改变, 反之亦然？这可以针对给定数据集中的数字和分类特征进行测试. 我们也可能想要确定以后的目标和工作流程阶段的生存以外的特征之间的相关性. 关联某些特征可能有助于创建, 完善或纠正特征。\\n\",\n    \"\\n\",\n    \"**Converting（转换）.** 对于建模阶段, 需要准备数据. 根据模型算法的选择, 可能需要将所有特征转换为数值等价值. 所以例如将文本分类值转换为数字的值.\\n\",\n    \"\\n\",\n    \"**Completing（完整）.** 数据准备也可能要求我们估计一个特征中的任何缺失值. 当没有缺失值时，模型算法可能效果最好.\\n\",\n    \"\\n\",\n    \"**Correcting（校正）.** 我们还可以分析给定的训练数据集以找出错误或者可能在特征内不准确的值, 并尝试对这些值进行校正或排除包含错误的样本. 一种方法是检测样本或特征中的任何异常值. 如果对分析没有贡献, 或者可能会显着扭曲结果, 我们也可能完全丢弃一个特征.\\n\",\n    \"\\n\",\n    \"**Creating（创建）.** 我们可以根据现有特征或一组特征来创建新特征, 以便新特征遵循 correlation（相关）, conversion（转换）, completeness（完整）的目标.\\n\",\n    \"\\n\",\n    \"**Charting（绘图）.** 如何根据数据的性质和解决方案的目标来选择正确的可视化图表工具以及绘图.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"56a3be4e-76ef-20c6-25e8-da16147cf6d7\",\n    \"_uuid\": \"9c7643f2494b7eb0db4e1a5a7696ff3e993352b8\",\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## 重构的发布日期 2017年1月29日\\n\",\n    \"\\n\",\n    \"We are significantly refactoring the notebook based on (a) comments received by readers, (b) issues in porting notebook from Jupyter kernel (2.7) to Kaggle kernel (3.5), and (c) review of few more best practice kernels.\\n\",\n    \"\\n\",\n    \"### 用户评论\\n\",\n    \"\\n\",\n    \"- Combine training and test data for certain operations like converting titles across dataset to numerical values. (thanks @Sharan Naribole)\\n\",\n    \"- Correct observation - nearly 30% of the passengers had siblings and/or spouses aboard. (thanks @Reinhard)\\n\",\n    \"- Correctly interpreting logistic regresssion coefficients. (thanks @Reinhard)\\n\",\n    \"\\n\",\n    \"### 移植问题\\n\",\n    \"\\n\",\n    \"- Specify plot dimensions, bring legend into plot.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"### 最佳实践\\n\",\n    \"\\n\",\n    \"- 在项目早期进行特征相关分析.\\n\",\n    \"- 为了可读性, 使用多个图而不是覆盖图.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"_cell_guid\": \"5767a33c-8f18-4034-e52d-bf7a8f7d8ab8\",\n    \"_uuid\": \"5ff730724498e5e39a020d13bebcceeb2128465b\",\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# 数据分析和整理\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"import random as rnd\\n\",\n    \"\\n\",\n    \"# 可视化\\n\",\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"# 机器学习\\n\",\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"from sklearn.svm import SVC, LinearSVC\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n    \"from sklearn.naive_bayes import GaussianNB\\n\",\n    \"from sklearn.linear_model import Perceptron\\n\",\n    \"from sklearn.linear_model import SGDClassifier\\n\",\n    \"from sklearn.tree import DecisionTreeClassifier\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"6b5dc743-15b1-aac6-405e-081def6ecca1\",\n    \"_uuid\": \"4060db2c31aae28179c44383a0870842045806e3\"\n   },\n   \"source\": [\n    \"## 获取数据\\n\",\n    \"\\n\",\n    \"Python 的 Pandas 包帮助我们处理我们的数据集.\\n\",\n    \"我们首先将训练和测试数据集收集到 Pandas DataFrame 中.\\n\",\n    \"我们还将这些数据集组合在一起, 在两个数据集上运行某些操作.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"_cell_guid\": \"e7319668-86fe-8adc-438d-0eef3fd0a982\",\n    \"_uuid\": \"394ed5fda2730c9819ab4b3271a197c1c6f63bca\",\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_df = pd.read_csv('../input/train.csv')\\n\",\n    \"test_df = pd.read_csv('../input/test.csv')\\n\",\n    \"combine = [train_df, test_df]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"3d6188f3-dc82-8ae6-dabd-83e28fcbf10d\",\n    \"_uuid\": \"6319a800eb192dca6fed152fc63ae7127d3de4a8\"\n   },\n   \"source\": [\n    \"## 通过 describing（描述）数据进行分析\\n\",\n    \"\\n\",\n    \"在我们的项目早期, Pandas 还帮助描述回答数据集中的以下问题.\\n\",\n    \"\\n\",\n    \"**数据集中哪些特征是可用的?**\\n\",\n    \"\\n\",\n    \"注意: 直接操作或分析这些特征的名称.\\n\",\n    \"这些特征名称在 [Kaggle 数据页面](https://www.kaggle.com/c/titanic/data) 页面上有描述.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"_cell_guid\": \"ce473d29-8d19-76b8-24a4-48c217286e42\",\n    \"_uuid\": \"6ca47bb664dde8eeb7fd6db2194ffbc58d7b4e9c\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\\n\",\n      \" 'Ticket' 'Fare' 'Cabin' 'Embarked']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(train_df.columns.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"cd19a6f6-347f-be19-607b-dca950590b37\",\n    \"_uuid\": \"6d633da86183125117bd25785e185329444fa106\"\n   },\n   \"source\": [\n    \"**哪些特征是 categorical（分类的）?**\\n\",\n    \"\\n\",\n    \"这些值将样本分成几组相似的样本.\\n\",\n    \"在分类特征中的值是 nominal（标称的）, ordinal（顺序的）或 ratio（比例的）还是 interval based（基于区间的）值？\\n\",\n    \"除此之外, 这有助于我们选择合适的图表进行可视化.\\n\",\n    \"\\n\",\n    \"- Categorical（分类的）: Survived, Sex, and Embarked. Ordinal（顺序的）: Pclass.\\n\",\n    \"\\n\",\n    \"**哪些特征是 numerical（数值的）?**\\n\",\n    \"\\n\",\n    \"哪些特征是数值的？\\n\",\n    \"这些值随样本而变化.\\n\",\n    \"在数值特征中的值是 discrete（离散的）和 continuous（连续的） 还是 timeseries based（基于时间序列的）？\\n\",\n    \"\\n\",\n    \"- Continous（连续的）: Age, Fare. Discrete（离散的）: SibSp, Parch.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"_cell_guid\": \"8d7ac195-ac1a-30a4-3f3f-80b8cf2c1c0f\",\n    \"_uuid\": \"7ddf8763ea0486359b22711ffada0f7b1201a7da\",\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Braund, Mr. Owen Harris</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>22.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>38.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C85</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Heikkinen, Miss. Laina</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>26.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>C123</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Allen, Mr. William Henry</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  \\\\\\n\",\n       \"0            1         0       3   \\n\",\n       \"1            2         1       1   \\n\",\n       \"2            3         1       3   \\n\",\n       \"3            4         1       1   \\n\",\n       \"4            5         0       3   \\n\",\n       \"\\n\",\n       \"                                                Name     Sex   Age  SibSp  \\\\\\n\",\n       \"0                            Braund, Mr. Owen Harris    male  22.0      1   \\n\",\n       \"1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \\n\",\n       \"2                             Heikkinen, Miss. Laina  female  26.0      0   \\n\",\n       \"3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \\n\",\n       \"4                           Allen, Mr. William Henry    male  35.0      0   \\n\",\n       \"\\n\",\n       \"   Parch            Ticket     Fare Cabin Embarked  \\n\",\n       \"0      0         A/5 21171   7.2500   NaN        S  \\n\",\n       \"1      0          PC 17599  71.2833   C85        C  \\n\",\n       \"2      0  STON/O2. 3101282   7.9250   NaN        S  \\n\",\n       \"3      0            113803  53.1000  C123        S  \\n\",\n       \"4      0            373450   8.0500   NaN        S  \"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 预览数据\\n\",\n    \"train_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"97f4e6f8-2fea-46c4-e4e8-b69062ee3d46\",\n    \"_uuid\": \"1648e560e93ea947298439d01598524155bbe743\"\n   },\n   \"source\": [\n    \"**哪些特征是混合的数据类型?**\\n\",\n    \"\\n\",\n    \"相同特征中的 numerical（数值的）, alphanumeric（字母数值的）.\\n\",\n    \"这些是校正目标的候选特征.\\n\",\n    \"\\n\",\n    \"- Ticket 是numerical（数值的）和 alphanumeric（字母数值的）数据类型的混合类型. Cabin 是 alphanumeric（字母数值的）.\\n\",\n    \"\\n\",\n    \"**哪些特征也许包含错误或拼写错误?**\\n\",\n    \"\\n\",\n    \"对于一个大型的数据集来说, 这是很难审查的, 但是从较小的数据集中查看一些样本可能会直接告诉我们, 哪些特征可能需要校正.\\n\",\n    \"\\n\",\n    \"- Name 特征也许包含错误或拼写错误, 因为有几种方法可以用来描述名称, 包括头衔，圆括号和用于替代或短名称的引号.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"_cell_guid\": \"f6e761c2-e2ff-d300-164c-af257083bb46\",\n    \"_uuid\": \"030837af7736facdcc436275e13576130c10f9a7\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>886</th>\\n\",\n       \"      <td>887</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Montvila, Rev. Juozas</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>27.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>211536</td>\\n\",\n       \"      <td>13.00</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>887</th>\\n\",\n       \"      <td>888</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Graham, Miss. Margaret Edith</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>19.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>112053</td>\\n\",\n       \"      <td>30.00</td>\\n\",\n       \"      <td>B42</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>888</th>\\n\",\n       \"      <td>889</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Johnston, Miss. Catherine Helen \\\"Carrie\\\"</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>W./C. 6607</td>\\n\",\n       \"      <td>23.45</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>889</th>\\n\",\n       \"      <td>890</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Behr, Mr. Karl Howell</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>26.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>111369</td>\\n\",\n       \"      <td>30.00</td>\\n\",\n       \"      <td>C148</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>890</th>\\n\",\n       \"      <td>891</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Dooley, Mr. Patrick</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>32.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>370376</td>\\n\",\n       \"      <td>7.75</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"     PassengerId  Survived  Pclass                                      Name  \\\\\\n\",\n       \"886          887         0       2                     Montvila, Rev. Juozas   \\n\",\n       \"887          888         1       1              Graham, Miss. Margaret Edith   \\n\",\n       \"888          889         0       3  Johnston, Miss. Catherine Helen \\\"Carrie\\\"   \\n\",\n       \"889          890         1       1                     Behr, Mr. Karl Howell   \\n\",\n       \"890          891         0       3                       Dooley, Mr. Patrick   \\n\",\n       \"\\n\",\n       \"        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \\n\",\n       \"886    male  27.0      0      0      211536  13.00   NaN        S  \\n\",\n       \"887  female  19.0      0      0      112053  30.00   B42        S  \\n\",\n       \"888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \\n\",\n       \"889    male  26.0      0      0      111369  30.00  C148        C  \\n\",\n       \"890    male  32.0      0      0      370376   7.75   NaN        Q  \"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_df.tail()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"8bfe9610-689a-29b2-26ee-f67cd4719079\",\n    \"_uuid\": \"2980c677698d5b219f3eca1faa748d4ef1c06b3f\"\n   },\n   \"source\": [\n    \"**哪些特征包含 blank（空格）, null（无效的）或 empty values（空值）?**\\n\",\n    \"\\n\",\n    \"这些将需要校正.\\n\",\n    \"\\n\",\n    \"- Cabin > Age > Embarked features contain a number of null values in that order for the training dataset.\\n\",\n    \"- Cabin > Age are incomplete in case of test dataset.\\n\",\n    \"\\n\",\n    \"**各个特征的数据类型是什么样的?**\\n\",\n    \"\\n\",\n    \"在转换的目标时可以帮助我们.\\n\",\n    \"\\n\",\n    \"- 7 个特征是 integer 或 floats. 6 个在测试数据集中.\\n\",\n    \"- 5 个特征是 strings (object).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"_cell_guid\": \"9b805f69-665a-2b2e-f31d-50d87d52865d\",\n    \"_uuid\": \"0e026227df676ac169811999565a86288fa129b1\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"RangeIndex: 891 entries, 0 to 890\\n\",\n      \"Data columns (total 12 columns):\\n\",\n      \"PassengerId    891 non-null int64\\n\",\n      \"Survived       891 non-null int64\\n\",\n      \"Pclass         891 non-null int64\\n\",\n      \"Name           891 non-null object\\n\",\n      \"Sex            891 non-null object\\n\",\n      \"Age            714 non-null float64\\n\",\n      \"SibSp          891 non-null int64\\n\",\n      \"Parch          891 non-null int64\\n\",\n      \"Ticket         891 non-null object\\n\",\n      \"Fare           891 non-null float64\\n\",\n      \"Cabin          204 non-null object\\n\",\n      \"Embarked       889 non-null object\\n\",\n      \"dtypes: float64(2), int64(5), object(5)\\n\",\n      \"memory usage: 83.6+ KB\\n\",\n      \"________________________________________\\n\",\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"RangeIndex: 418 entries, 0 to 417\\n\",\n      \"Data columns (total 11 columns):\\n\",\n      \"PassengerId    418 non-null int64\\n\",\n      \"Pclass         418 non-null int64\\n\",\n      \"Name           418 non-null object\\n\",\n      \"Sex            418 non-null object\\n\",\n      \"Age            332 non-null float64\\n\",\n      \"SibSp          418 non-null int64\\n\",\n      \"Parch          418 non-null int64\\n\",\n      \"Ticket         418 non-null object\\n\",\n      \"Fare           417 non-null float64\\n\",\n      \"Cabin          91 non-null object\\n\",\n      \"Embarked       418 non-null object\\n\",\n      \"dtypes: float64(2), int64(4), object(5)\\n\",\n      \"memory usage: 36.0+ KB\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_df.info()\\n\",\n    \"print('_'*40)\\n\",\n    \"test_df.info()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"859102e1-10df-d451-2649-2d4571e5f082\",\n    \"_uuid\": \"241d3667c3850d6d5ed83ffab6991f185a44cdec\"\n   },\n   \"source\": [\n    \"**样本中数值特征值的分布是什么?**\\n\",\n    \"\\n\",\n    \"这有助于我们确定, 除了其他早期的思考, 在实际问题领域的训练数据集是如何具有代表性的.\\n\",\n    \"\\n\",\n    \"- 总样本是 891 或者在泰坦尼克号（2,224）上实际旅客的 40%.\\n\",\n    \"- Survived（生存）是一个具有 0 或 1 值的分类特征.\\n\",\n    \"- 大约 38% 样本幸存了下来, 然而实际的幸存率是 32%.\\n\",\n    \"- 大多数旅客 (> 75%) 没有和父母或孩子一起旅行.\\n\",\n    \"- 近 30% 的旅客有兄弟姐妹 和/或 配偶.\\n\",\n    \"- 少数旅客 Fares（票价）差异显著 (<1%), 最高达 $512.\\n\",\n    \"- 很少有年长的旅客 (<1%) 在年龄范围 65-80.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"_cell_guid\": \"58e387fe-86e4-e068-8307-70e37fe3f37b\",\n    \"_uuid\": \"c4bd85c7847593602a09d809337af4616d8c9e02\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"      <td>714.000000</td>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <td>446.000000</td>\\n\",\n       \"      <td>0.383838</td>\\n\",\n       \"      <td>2.308642</td>\\n\",\n       \"      <td>29.699118</td>\\n\",\n       \"      <td>0.523008</td>\\n\",\n       \"      <td>0.381594</td>\\n\",\n       \"      <td>32.204208</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>std</th>\\n\",\n       \"      <td>257.353842</td>\\n\",\n       \"      <td>0.486592</td>\\n\",\n       \"      <td>0.836071</td>\\n\",\n       \"      <td>14.526497</td>\\n\",\n       \"      <td>1.102743</td>\\n\",\n       \"      <td>0.806057</td>\\n\",\n       \"      <td>49.693429</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.420000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25%</th>\\n\",\n       \"      <td>223.500000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>2.000000</td>\\n\",\n       \"      <td>20.125000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>7.910400</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>50%</th>\\n\",\n       \"      <td>446.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>3.000000</td>\\n\",\n       \"      <td>28.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>14.454200</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>75%</th>\\n\",\n       \"      <td>668.500000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>3.000000</td>\\n\",\n       \"      <td>38.000000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>31.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <td>891.000000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>3.000000</td>\\n\",\n       \"      <td>80.000000</td>\\n\",\n       \"      <td>8.000000</td>\\n\",\n       \"      <td>6.000000</td>\\n\",\n       \"      <td>512.329200</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       PassengerId    Survived      Pclass         Age       SibSp  \\\\\\n\",\n       \"count   891.000000  891.000000  891.000000  714.000000  891.000000   \\n\",\n       \"mean    446.000000    0.383838    2.308642   29.699118    0.523008   \\n\",\n       \"std     257.353842    0.486592    0.836071   14.526497    1.102743   \\n\",\n       \"min       1.000000    0.000000    1.000000    0.420000    0.000000   \\n\",\n       \"25%     223.500000    0.000000    2.000000   20.125000    0.000000   \\n\",\n       \"50%     446.000000    0.000000    3.000000   28.000000    0.000000   \\n\",\n       \"75%     668.500000    1.000000    3.000000   38.000000    1.000000   \\n\",\n       \"max     891.000000    1.000000    3.000000   80.000000    8.000000   \\n\",\n       \"\\n\",\n       \"            Parch        Fare  \\n\",\n       \"count  891.000000  891.000000  \\n\",\n       \"mean     0.381594   32.204208  \\n\",\n       \"std      0.806057   49.693429  \\n\",\n       \"min      0.000000    0.000000  \\n\",\n       \"25%      0.000000    7.910400  \\n\",\n       \"50%      0.000000   14.454200  \\n\",\n       \"75%      0.000000   31.000000  \\n\",\n       \"max      6.000000  512.329200  \"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_df.describe()\\n\",\n    \"# Review survived rate using `percentiles=[.61, .62]` knowing our problem description mentions 38% survival rate.\\n\",\n    \"# Review Parch distribution using `percentiles=[.75, .8]`\\n\",\n    \"# SibSp distribution `[.68, .69]`\\n\",\n    \"# Age and Fare `[.1, .2, .3, .4, .5, .6, .7, .8, .9, .99]`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"5462bc60-258c-76bf-0a73-9adc00a2f493\",\n    \"_uuid\": \"93ac7caf79ef8a43bf2f88e1b15e70b9521fea04\"\n   },\n   \"source\": [\n    \"**分类特征的分布是什么样的?**\\n\",\n    \"\\n\",\n    \"- Names（名称）特征在数据集中是唯一的 (count=unique=891)\\n\",\n    \"- Sex（性别）变量有两个可能的值, 男性为 65% (top=male, freq=577/count=891).\\n\",\n    \"- Cabin（房间号）值在样本中有重复. 或者几个旅客共享一个客舱.\\n\",\n    \"- Embarked（出发港）有 3 个可能的值. 大多数乘客使用 S 港口(top=S)\\n\",\n    \"- Ticket（船票号码）特征有很高 (22%) 的重复值 (unique=681).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"_cell_guid\": \"8066b378-1964-92e8-1352-dcac934c6af3\",\n    \"_uuid\": \"eccc6479282828bd97b4d1f391ff09eded517ce1\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <td>891</td>\\n\",\n       \"      <td>891</td>\\n\",\n       \"      <td>891</td>\\n\",\n       \"      <td>204</td>\\n\",\n       \"      <td>889</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>unique</th>\\n\",\n       \"      <td>891</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>681</td>\\n\",\n       \"      <td>147</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>top</th>\\n\",\n       \"      <td>Mitchell, Mr. Henry Michael</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>1601</td>\\n\",\n       \"      <td>C23 C25 C27</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>freq</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>577</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>644</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                               Name   Sex Ticket        Cabin Embarked\\n\",\n       \"count                           891   891    891          204      889\\n\",\n       \"unique                          891     2    681          147        3\\n\",\n       \"top     Mitchell, Mr. Henry Michael  male   1601  C23 C25 C27        S\\n\",\n       \"freq                              1   577      7            4      644\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_df.describe(include=['O'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"2cb22b88-937d-6f14-8b06-ea3361357889\",\n    \"_uuid\": \"e7b4b885268793a3f7f818a28e86aa97bdc84a69\"\n   },\n   \"source\": [\n    \"### 基于数据分析的假设\\n\",\n    \"\\n\",\n    \"到目前为止, 基于数据分析, 我们得出以下假设.\\n\",\n    \"在采取适当的行动之前, 我们可能会进一步验证这些假设.\\n\",\n    \"\\n\",\n    \"**Correlating（相关）.**\\n\",\n    \"\\n\",\n    \"我们想知道每个特征与生存相关的程度.\\n\",\n    \"我们希望在项目早期做到这一点, 并将这些快速相关性与项目后期的模型相关性相匹配.\\n\",\n    \"\\n\",\n    \"**Completing（完整）.**\\n\",\n    \"\\n\",\n    \"1. 我们可能想要去补全丢失的 Age（年龄）特征，因为它肯定与生存相关.\\n\",\n    \"2. 我们也想要去补全丢失的 Embarked（出发港）特征, 因为它也可能与生存或者其它重要的特征相关联.\\n\",\n    \"\\n\",\n    \"**Correcting（校正）.**\\n\",\n    \"\\n\",\n    \"1. Ticket（船票号码）特征可能会从我们的分析中删除, 因为它包含了很高的重复比例 (22%), 并且票号和生存之间可能没有关联.\\n\",\n    \"2. Cabin（房间号）特征可能因为高度不完整而丢失, 或者在 训练和测试数据集中都包含许多 null 值.\\n\",\n    \"3. PassengerId（旅客ID）可能会从训练数据集中删除, 因为它对生存来说没有贡献.\\n\",\n    \"4. Name（名称）特征是比较不规范的, 可能不直接影响生产, 所以也许会删除.\\n\",\n    \"\\n\",\n    \"**Creating（创建）.**\\n\",\n    \"\\n\",\n    \"1. 我们可能希望创建一个名为 Family 的基于 Parch 和 SibSp 的新特征，以获取船上家庭成员的总数.\\n\",\n    \"2. 我们可能想要设计 Name 功能以将 Title 抽取为新特征.\\n\",\n    \"3. 我们可能要为 Age（年龄）段创建新的特征. 这将一个连续的数字特征转变为一个顺序的分类特征.\\n\",\n    \"4. 如果它有助于我们的分析, 我们也可能想要创建 Fare（票价）范围的特征。\\n\",\n    \"\\n\",\n    \"**Classifying（分类）.**\\n\",\n    \"\\n\",\n    \"根据前面提到的问题描述, 我们也可以增加我们的假设.\\n\",\n    \"\\n\",\n    \"1. Women (Sex=female) 更有可能幸存下来.\\n\",\n    \"2. Children (Age<?) 更有可能幸存下来. \\n\",\n    \"3. 上层阶级的旅客 (Pclass=1) 更有可能幸存下来.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"6db63a30-1d86-266e-2799-dded03c45816\",\n    \"_uuid\": \"ee691f202dcb1cafc9cae26049edb3b95f386e00\"\n   },\n   \"source\": [\n    \"## 通过旋转特征进行分析\\n\",\n    \"\\n\",\n    \"为了确认我们的一些观察和假设, 我们可以快速分析我们的特征之间的相互关系.\\n\",\n    \"我们只能在这个阶段为没有任何空值的特征做到这一点.\\n\",\n    \"对于 Sex（性别），顺序的（Pclass）或离散的（SibSp，Parch）类型的特征, 这也是有意义的.\\n\",\n    \"\\n\",\n    \"- **Pclass** 我们观察到 Pclass = 1 和 Survived（分类＃3）之间的显着相关性（> 0.5）. 我们决定在我们的模型中包含这个特征.\\n\",\n    \"- **Sex** 在 Sex=female（性别=女性）的问题定义中确认了74％（分类＃1）的幸存率非常高的观察意见.\\n\",\n    \"- **SibSp and Parch** 这些特征对于某些值具有零相关性. 从这些单独的特征（创建＃1）派生一个特征或一组特征可能是最好的\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"_cell_guid\": \"0964832a-a4be-2d6f-a89e-63526389cee9\",\n    \"_uuid\": \"62fb816bd0d5612f6b12cce8ed46036acb75527b\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.629630</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.472826</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.242363</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Pclass  Survived\\n\",\n       \"0       1  0.629630\\n\",\n       \"1       2  0.472826\\n\",\n       \"2       3  0.242363\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"_cell_guid\": \"68908ba6-bfe9-5b31-cfde-6987fc0fbe9a\",\n    \"_uuid\": \"2cfacba29530bf3e52fedf89c5bdd12eb9a924c3\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>0.742038</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>0.188908</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      Sex  Survived\\n\",\n       \"0  female  0.742038\\n\",\n       \"1    male  0.188908\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_df[[\\\"Sex\\\", \\\"Survived\\\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"_cell_guid\": \"01c06927-c5a6-342a-5aa8-2e486ec3fd7c\",\n    \"_uuid\": \"c0f9f9a3d89294ba3073f040d12a9625d26af1f1\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.535885</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.464286</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.345395</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.250000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.166667</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SibSp  Survived\\n\",\n       \"1      1  0.535885\\n\",\n       \"2      2  0.464286\\n\",\n       \"0      0  0.345395\\n\",\n       \"3      3  0.250000\\n\",\n       \"4      4  0.166667\\n\",\n       \"5      5  0.000000\\n\",\n       \"6      8  0.000000\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_df[[\\\"SibSp\\\", \\\"Survived\\\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"_cell_guid\": \"e686f98b-a8c9-68f8-36a4-d4598638bbd5\",\n    \"_uuid\": \"3c6193e348b7616bfa8624ca9562a7ad5749906d\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.600000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.550847</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.500000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.343658</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.200000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Parch  Survived\\n\",\n       \"3      3  0.600000\\n\",\n       \"1      1  0.550847\\n\",\n       \"2      2  0.500000\\n\",\n       \"0      0  0.343658\\n\",\n       \"5      5  0.200000\\n\",\n       \"4      4  0.000000\\n\",\n       \"6      6  0.000000\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_df[[\\\"Parch\\\", \\\"Survived\\\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"0d43550e-9eff-3859-3568-8856570eff76\",\n    \"_uuid\": \"0a396ef5d708292e88f66829bd4d3f3d48465e36\"\n   },\n   \"source\": [\n    \"## 通过可视化数据进行分析\\n\",\n    \"\\n\",\n    \"现在我们可以继续使用可视化分析数据来确认我们的一些假设.\\n\",\n    \"\\n\",\n    \"### 关联数值的特征\\n\",\n    \"\\n\",\n    \"让我们从理解数值的特征和解决方案目标（生存）之间的相关性开始.\\n\",\n    \"\\n\",\n    \"柱状图可用于分析连续的数字变量，如 Age（年龄），其中条带或范围将有助于识别有用的模式.\\n\",\n    \"直方图可以使用自动定义的 bins 或等分范围的 bins 来说明样本的分布.\\n\",\n    \"这有助于我们回答有关特定频段的问题（婴儿有更好的幸存率吗？）\\n\",\n    \"\\n\",\n    \"请注意，直方图可视化中的 x 轴表示样本或旅客的数量.\\n\",\n    \"\\n\",\n    \"**Observations（观察）.**\\n\",\n    \"\\n\",\n    \"- 婴儿（4 岁以下）存活率高.\\n\",\n    \"- 最老的乘客（年龄= 80）幸存下来.\\n\",\n    \"- 大量的 15-25 岁的孩子没有幸.\\n\",\n    \"- 大多数乘客在 15-35 年龄范围内.\\n\",\n    \"\\n\",\n    \"**Decisions（决策）.**\\n\",\n    \"\\n\",\n    \"这个简单的分析证实了我们的假设, 作为后续工作流程阶段的决策.\\n\",\n    \"\\n\",\n    \"- 在我们的模型训练中, 我们应该考虑年龄（我们假设分类＃2）.\\n\",\n    \"- 完成空值的年龄功能（完成＃1）.\\n\",\n    \"- 我们应该 band（组合）年龄组（创建＃3）.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"_cell_guid\": \"50294eac-263a-af78-cb7e-3778eb9ad41f\",\n    \"_uuid\": \"5e581f2b5be92a31c510f7db4bfceb3c9727c473\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0xce233c8>\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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y64jzK+/ZJcmeSb3bl8ZUvnMMnfdP++dyb5TJK9WzuHgzCPFhRrs7k0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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xce23e48>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"g = sns.FacetGrid(train_df, col='Survived')\\n\",\n    \"g.map(plt.hist, 'Age', bins=20)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"87096158-4017-9213-7225-a19aea67a800\",\n    \"_uuid\": \"f3b60c4ce160be82ebd9ad8ff14fcf2578ff2b03\"\n   },\n   \"source\": [\n    \"### 关联数字和顺序的特征\\n\",\n    \"\\n\",\n    \"我们可以结合多个特征使用一个图来确定其相关性.\\n\",\n    \"这可以通过具有数字值的数字和分类特征来完成。\\n\",\n    \"\\n\",\n    \"**Observations（观察）.**\\n\",\n    \"\\n\",\n    \"- Pclass=3 拥有最多的乘客，但大多数没有生存. 确认我们的分类假设 ＃2.\\n\",\n    \"- Pclass=2 和 Pclass = 3 的婴儿乘客大多存活. 进一步限定了我们的分类假设 ＃2.\\n\",\n    \"- Pclass=1 的大多数乘客幸存下来。 确认我们的分类假设 ＃3。\\n\",\n    \"- Pclass 在乘客的年龄分布方面有所不同.\\n\",\n    \"\\n\",\n    \"**Decisions（决策）.**\\n\",\n    \"\\n\",\n    \"- 考虑 Pclass 用于模型训练.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"_cell_guid\": \"916fdc6b-0190-9267-1ea9-907a3d87330d\",\n    \"_uuid\": \"c1c736a54c925c1d1db4133b30ebe144aabc295f\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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QBJkjpoZAqAcb3EaJL9k1yZZGOSm5Oc1rbvneTyJJva272GHWsvkixL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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xd5eceb8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')\\n\",\n    \"grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)\\n\",\n    \"grid.map(plt.hist, 'Age', alpha=.5, bins=20)\\n\",\n    \"grid.add_legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"36f5a7c0-c55c-f76f-fdf8-945a32a68cb0\",\n    \"_uuid\": \"5cdb1388a846ad8b19a2c649da034f4f45cae31b\"\n   },\n   \"source\": [\n    \"### 关联分类特征\\n\",\n    \"\\n\",\n    \"现在我们可以将分类特征与我们的解决方案目标关联起来.\\n\",\n    \"\\n\",\n    \"**Observations（观察）.**\\n\",\n    \"\\n\",\n    \"- Female（女性）旅客的幸存率比 male（男性）好得多. 确认分类（＃1）。\\n\",\n    \"- Embarked= C 的例外, 其中男性的成活率较高. 这可能是 Pclass 和 Embarked 之间的相关性, 反过来, Pclass 和 Survived 之间, 不一定是Embarked和 Survived之间的相关性。\\n\",\n    \"- 与 C 和 Q 港口的 Pclass = 2 相比, Pclass = 3 时男性的生存率更高. 完成（＃2）。\\n\",\n    \"- 出发港口的 Pclass=3 和男性乘客的生存率不同. 相关（＃1）。\\n\",\n    \"\\n\",\n    \"**Decisions（决策）.**\\n\",\n    \"\\n\",\n    \"- 增加 Sex 特征以用于模型训练.\\n\",\n    \"- 补全丢失值并添加 Embarked 特征以用于模型训练.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"_cell_guid\": \"db57aabd-0e26-9ff9-9ebd-56d401cdf6e8\",\n    \"_uuid\": \"cb86b6c046ba3b91dfc8811b08caefd515b7f1bb\",\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0xd6625f8>\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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aGQ69mJyEQR+ROwAojHeXysoXUaaqV4k4jkB7VMvCVo3tdFZIv7+nqo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\\ngg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xd84a0f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# grid = sns.FacetGrid(train_df, col='Embarked')\\n\",\n    \"grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\\n\",\n    \"grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\\n\",\n    \"grid.add_legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"6b3f73f4-4600-c1ce-34e0-bd7d9eeb074a\",\n    \"_uuid\": \"6e031e6217b278363f1bd659b2e591bcb30562bd\"\n   },\n   \"source\": [\n    \"### 关联分类和数值的特征\\n\",\n    \"\\n\",\n    \"我们也可能想要关联分类特征（非数值的）和数值的特征.\\n\",\n    \"我们可以考虑将 Embarked（类别非数字）, Sex（类别非数字）, Fare（数字连续）与生存（分类数字）相关联.\\n\",\n    \"\\n\",\n    \"**Observations（观察）.**\\n\",\n    \"\\n\",\n    \"- Higher fare paying passengers had better survival. Confirms our assumption for creating (#4) fare ranges.\\n\",\n    \"- Port of embarkation correlates with survival rates. Confirms correlating (#1) and completing (#2).\\n\",\n    \"\\n\",\n    \"- 更高的票价付费旅客有更好的生存. 证实我们对创造（＃4）票价范围的假设.\\n\",\n    \"- 搭乘港口与生存率相关. 确认关联（＃1）和完成（＃2）.\\n\",\n    \"\\n\",\n    \"**Decisions（决策）.**\\n\",\n    \"\\n\",\n    \"- 考虑关联Fare(票价)特征\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"_cell_guid\": \"a21f66ac-c30d-f429-cc64-1da5460d16a9\",\n    \"_uuid\": \"af9099be9d7fe9f358ccdcf704991abde0880409\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0xd5cb198>\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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gogwCnATVX1ib6XzgWOacaPAc5pKwZJw2VeS+OjzVMA+wOvB25Isq5v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\\nTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xd7e61d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})\\n\",\n    \"grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\\n\",\n    \"grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\\n\",\n    \"grid.add_legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"cfac6291-33cc-506e-e548-6cad9408623d\",\n    \"_uuid\": \"53c762e996de3aefbe4e7e70671a43930189d9b9\"\n   },\n   \"source\": [\n    \"## 整理数据\\n\",\n    \"\\n\",\n    \"我们收集了关于我们的数据集和解决方案要求的一些假设和决策.\\n\",\n    \"到目前为止, 我们没有必要改变一个单个的特征或值来达到目标.\\n\",\n    \"让我们现在执行我们的决定和假设来 correcting(校正), creating（创建）和 completing（完整）目标.\\n\",\n    \"\\n\",\n    \"### 通过删除特征进行校正\\n\",\n    \"\\n\",\n    \"这是一个很好的开始执行目标. 通过丢弃特征, 我们正在处理更少的数据点. 加快我们的 notebook, 并简化分析.\\n\",\n    \"\\n\",\n    \"根据我们的假设和决策, 我们要放弃 Cabin（房间号）（更正＃2）和 Ticket（票号）（更正＃1）的特征.\\n\",\n    \"\\n\",\n    \"请注意, 如果适用, 我们将对训练和测试数据集进行操作, 以保持一致.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"_cell_guid\": \"da057efe-88f0-bf49-917b-bb2fec418ed9\",\n    \"_uuid\": \"6c7b899f23e0c2cb0b2b05447eece4f0aab769f5\",\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Before (891, 12) (418, 11) (891, 12) (418, 11)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"('After', (891, 10), (418, 9), (891, 10), (418, 9))\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Before\\\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)\\n\",\n    \"\\n\",\n    \"train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\\n\",\n    \"test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\\n\",\n    \"combine = [train_df, test_df]\\n\",\n    \"\\n\",\n    \"\\\"After\\\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"6b3a1216-64b6-7fe2-50bc-e89cc964a41c\",\n    \"_uuid\": \"762ea4993c2d5c7b9667521badc4bb0802f35a24\"\n   },\n   \"source\": [\n    \"### 从现在的提取以创建性特征\\n\",\n    \"\\n\",\n    \"我们想要分析一下, Name 特征是否可以被设计来提取 titles（头衔）和测试titles与survival（生存）之间的相关性, 然后再删除Name 和 PassengerId 特征.\\n\",\n    \"\\n\",\n    \"在下面的代码中, 我们使用正则表达式提取 Title 特征.  正则表达式`(\\\\w+\\\\.)`匹配 Name 特征中以点号字符结尾的第一个单词.\\n\",\n    \"`expand = False` 标志返回一个 DataFrame.\\n\",\n    \"\\n\",\n    \"**Observations（观察）.**\\n\",\n    \"\\n\",\n    \"当我们绘制出 Title, Age 和 Survived 的图时, 我们可以发现以下观察.\\n\",\n    \"\\n\",\n    \"- 大多数 titles band 年龄组准确. 例如: Master Title的Age平均为5岁。\\n\",\n    \"- Title 中的生存年龄段略有不同.\\n\",\n    \"- 某些 Title 大多存活(Mme, Lady, Sir)或某些Title没有存活(Don, Rev, Jonkheer)\\n\",\n    \"\\n\",\n    \"**Decision（决策）.**\\n\",\n    \"\\n\",\n    \"- 我们决定保留模型训练的新 Title 特征.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"_cell_guid\": \"df7f0cd4-992c-4a79-fb19-bf6f0c024d4b\",\n    \"_uuid\": \"6e1e16b53b683ba5c4e0b7f22ff97209d12f8e0c\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>female</th>\\n\",\n       \"      <th>male</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Capt</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Col</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Countess</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Don</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Dr</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Jonkheer</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Lady</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Major</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Master</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Miss</th>\\n\",\n       \"      <td>182</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Mlle</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Mme</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Mr</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>517</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Mrs</th>\\n\",\n       \"      <td>125</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Ms</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Rev</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Sir</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"Sex       female  male\\n\",\n       \"Title                 \\n\",\n       \"Capt           0     1\\n\",\n       \"Col            0     2\\n\",\n       \"Countess       1     0\\n\",\n       \"Don            0     1\\n\",\n       \"Dr             1     6\\n\",\n       \"Jonkheer       0     1\\n\",\n       \"Lady           1     0\\n\",\n       \"Major          0     2\\n\",\n       \"Master         0    40\\n\",\n       \"Miss         182     0\\n\",\n       \"Mlle           2     0\\n\",\n       \"Mme            1     0\\n\",\n       \"Mr             0   517\\n\",\n       \"Mrs          125     0\\n\",\n       \"Ms             1     0\\n\",\n       \"Rev            0     6\\n\",\n       \"Sir            0     1\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for dataset in combine:\\n\",\n    \"    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\\\.', expand=False)\\n\",\n    \"\\n\",\n    \"pd.crosstab(train_df['Title'], train_df['Sex'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"908c08a6-3395-19a5-0cd7-13341054012a\",\n    \"_uuid\": \"a780a39e03cc679742db03ad2531cff65492cc20\"\n   },\n   \"source\": [\n    \"我们可以用更常见的Title来替换很多Title, 或者将它们分类为 `Rare`(稀有).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"_cell_guid\": \"553f56d7-002a-ee63-21a4-c0efad10cfe9\",\n    \"_uuid\": \"a77aab132073d20a9050d63a57aab0b77931cf98\",\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>Master</td>\\n\",\n       \"      <td>0.575000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>Miss</td>\\n\",\n       \"      <td>0.702703</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>Mr</td>\\n\",\n       \"      <td>0.156673</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>Mrs</td>\\n\",\n       \"      <td>0.793651</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>Rare</td>\\n\",\n       \"      <td>0.347826</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    Title  Survived\\n\",\n       \"0  Master  0.575000\\n\",\n       \"1    Miss  0.702703\\n\",\n       \"2      Mr  0.156673\\n\",\n       \"3     Mrs  0.793651\\n\",\n       \"4    Rare  0.347826\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for dataset in combine:\\n\",\n    \"    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\\\\n\",\n    \" \\t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\\n\",\n    \"\\n\",\n    \"    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\\n\",\n    \"    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\\n\",\n    \"    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\\n\",\n    \"    \\n\",\n    \"train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"6d46be9a-812a-f334-73b9-56ed912c9eca\",\n    \"_uuid\": \"570174039502747cfc590e3a23b2b2407649c9df\"\n   },\n   \"source\": [\n    \"我们可以将 titles（头衔）转换为顺序的.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"_cell_guid\": \"67444ebc-4d11-bac1-74a6-059133b6e2e8\",\n    \"_uuid\": \"12d48fc3ce08e6daff750ecdf741852f7ada87fd\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Braund, Mr. Owen Harris</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>22.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>38.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Heikkinen, Miss. Laina</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>26.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Allen, Mr. William Henry</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  \\\\\\n\",\n       \"0            1         0       3   \\n\",\n       \"1            2         1       1   \\n\",\n       \"2            3         1       3   \\n\",\n       \"3            4         1       1   \\n\",\n       \"4            5         0       3   \\n\",\n       \"\\n\",\n       \"                                                Name     Sex   Age  SibSp  \\\\\\n\",\n       \"0                            Braund, Mr. Owen Harris    male  22.0      1   \\n\",\n       \"1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \\n\",\n       \"2                             Heikkinen, Miss. Laina  female  26.0      0   \\n\",\n       \"3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \\n\",\n       \"4                           Allen, Mr. William Henry    male  35.0      0   \\n\",\n       \"\\n\",\n       \"   Parch     Fare Embarked  Title  \\n\",\n       \"0      0   7.2500        S      1  \\n\",\n       \"1      0  71.2833        C      3  \\n\",\n       \"2      0   7.9250        S      2  \\n\",\n       \"3      0  53.1000        S      3  \\n\",\n       \"4      0   8.0500        S      1  \"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"title_mapping = {\\\"Mr\\\": 1, \\\"Miss\\\": 2, \\\"Mrs\\\": 3, \\\"Master\\\": 4, \\\"Rare\\\": 5}\\n\",\n    \"for dataset in combine:\\n\",\n    \"    dataset['Title'] = dataset['Title'].map(title_mapping)\\n\",\n    \"    dataset['Title'] = dataset['Title'].fillna(0)\\n\",\n    \"\\n\",\n    \"train_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"f27bb974-a3d7-07a1-f7e4-876f6da87e62\",\n    \"_uuid\": \"ebbb6df8eeb4898e25cf2e375a5d5540906ba554\"\n   },\n   \"source\": [\n    \"现在我们可以放心地从训练和测试数据集中删除 Name 特征.\\n\",\n    \"我们也不需要训练数据集中的 PassengerId 特征.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"_cell_guid\": \"9d61dded-5ff0-5018-7580-aecb4ea17506\",\n    \"_uuid\": \"3699ea6473ef7e16779b28b319ccff0da2615e2f\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"((891, 9), (418, 9))\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\\n\",\n    \"test_df = test_df.drop(['Name'], axis=1)\\n\",\n    \"combine = [train_df, test_df]\\n\",\n    \"train_df.shape, test_df.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"2c8e84bb-196d-bd4a-4df9-f5213561b5d3\",\n    \"_uuid\": \"d55d78bc8cab25fa58194dbc46610e2395d1d3e0\"\n   },\n   \"source\": [\n    \"### 转换分类的特征\\n\",\n    \"\\n\",\n    \"现在我们可以将包含字符串的特征转换为数字值.\\n\",\n    \"这是大多数模型算法所要求的.\\n\",\n    \"这样做也将帮助我们实现特征完成目标.\\n\",\n    \"让我们开始将 Sex（性别）特征转换为名为 Gender（性别）的新特征, 其中 female=1, male=0.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"_cell_guid\": \"c20c1df2-157c-e5a0-3e24-15a828095c96\",\n    \"_uuid\": \"f61f46627bdef1b318c019c852f2e370f6bfd9e0\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>22.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>38.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>26.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\\n\",\n       \"0         0       3    0  22.0      1      0   7.2500        S      1\\n\",\n       \"1         1       1    1  38.0      1      0  71.2833        C      3\\n\",\n       \"2         1       3    1  26.0      0      0   7.9250        S      2\\n\",\n       \"3         1       1    1  35.0      1      0  53.1000        S      3\\n\",\n       \"4         0       3    0  35.0      0      0   8.0500        S      1\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for dataset in combine:\\n\",\n    \"    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\\n\",\n    \"\\n\",\n    \"train_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"d72cb29e-5034-1597-b459-83a9640d3d3a\",\n    \"_uuid\": \"2799e6b10611997bdeca13d86fcacced318853c9\"\n   },\n   \"source\": [\n    \"### 完整化数值字连续特征\\n\",\n    \"\\n\",\n    \"现在我们应该开始估计和完成缺少或空值的特征.\\n\",\n    \"我们将首先为 Age（年龄）特征执行此操作.\\n\",\n    \"\\n\",\n    \"我们可以考虑三种方法来完整化一个数值连续的特征.\\n\",\n    \"\\n\",\n    \"1.简单的方法是在平均值和 [标准偏差](https://en.wikipedia.org/wiki/Standard_deviation) 之间生成随机数.\\n\",\n    \"\\n\",\n    \"2.更准确地猜测缺失值的方法是使用其他相关特征. 在我们的例子中, 我们注意到 Age（年龄）, Sex（性别）和 Pclass 之间的相关性.猜测年龄值，使用Pclass和Gender特征组合中Age的[中位数](https://en.wikipedia.org/wiki/Median). 因此, Pclass=1 和 Gender=0，Pclass=1 和 Gender=1 的年龄中位数等等...\\n\",\n    \"\\n\",\n    \"3.结合方法 1 和 2. 因此. 不要根据中位数来猜测年龄值, 而应根据 Pclass 和 Sex 组合, 使用平均数和标准差之间的随机数.\\n\",\n    \"\\n\",\n    \"方法 1 和 3 将在我们的模型中引入随机噪声. 多次执行的结果可能会有所不同. 我们更喜欢方法 2.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"_cell_guid\": \"c311c43d-6554-3b52-8ef8-533ca08b2f68\",\n    \"_uuid\": \"3348a1cbf0c7e33e5b576f13a4aa881b103fd628\",\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0xece2e10>\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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2/Sa8vcBNzQTS+j937aFcDt3c+V4451EWN6AbC++/1xwJeBO4BPAQ8d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\\nNej/B+1qWsqKF3O5AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xece2748>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# grid = sns.FacetGrid(train_df, col='Pclass', hue='Gender')\\n\",\n    \"grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)\\n\",\n    \"grid.map(plt.hist, 'Age', alpha=.5, bins=20)\\n\",\n    \"grid.add_legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"a4f166f9-f5f9-1819-66c3-d89dd5b0d8ff\",\n    \"_uuid\": \"d3ef5e2578429c297e6f7eefa7c769a7cd59c564\"\n   },\n   \"source\": [\n    \"让我们开始准备一个空数组, 以包含基于 Pclass x Gender 组合以猜测 Age 值.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"_cell_guid\": \"9299523c-dcf1-fb00-e52f-e2fb860a3920\",\n    \"_uuid\": \"f28a31b2520fa6cb7684868464f96c8ed4f5897b\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.,  0.,  0.],\\n\",\n       \"       [ 0.,  0.,  0.]])\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"guess_ages = np.zeros((2,3))\\n\",\n    \"guess_ages\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"ec9fed37-16b1-5518-4fa8-0a7f579dbc82\",\n    \"_uuid\": \"4e6dab5115d513ce035864f6693a87c26897e47b\"\n   },\n   \"source\": [\n    \"现在我们迭代 Sex（0 或 1）和 Pclass（1, 2, 3）来计算 6 个组合的 Age 的猜测值.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"_cell_guid\": \"a4015dfa-a0ab-65bc-0cbe-efecf1eb2569\",\n    \"_uuid\": \"30fe7bd956f87da84afb5bc9bed792cc6dd0831f\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>22</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>38</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>26</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>35</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>35</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\\n\",\n       \"0         0       3    0   22      1      0   7.2500        S      1\\n\",\n       \"1         1       1    1   38      1      0  71.2833        C      3\\n\",\n       \"2         1       3    1   26      0      0   7.9250        S      2\\n\",\n       \"3         1       1    1   35      1      0  53.1000        S      3\\n\",\n       \"4         0       3    0   35      0      0   8.0500        S      1\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for dataset in combine:\\n\",\n    \"    for i in range(0, 2):\\n\",\n    \"        for j in range(0, 3):\\n\",\n    \"            guess_df = dataset[(dataset['Sex'] == i) & \\\\\\n\",\n    \"                                  (dataset['Pclass'] == j+1)]['Age'].dropna()\\n\",\n    \"\\n\",\n    \"            # age_mean = guess_df.mean()\\n\",\n    \"            # age_std = guess_df.std()\\n\",\n    \"            # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\\n\",\n    \"\\n\",\n    \"            age_guess = guess_df.median()\\n\",\n    \"\\n\",\n    \"            # Convert random age float to nearest .5 age\\n\",\n    \"            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\\n\",\n    \"            \\n\",\n    \"    for i in range(0, 2):\\n\",\n    \"        for j in range(0, 3):\\n\",\n    \"            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\\\\n\",\n    \"                    'Age'] = guess_ages[i,j]\\n\",\n    \"\\n\",\n    \"    dataset['Age'] = dataset['Age'].astype(int)\\n\",\n    \"\\n\",\n    \"train_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"dbe0a8bf-40bc-c581-e10e-76f07b3b71d4\",\n    \"_uuid\": \"ed080ed0742604812ae933aa2dcd9ffe0f752abf\"\n   },\n   \"source\": [\n    \"让我们创建年龄段并确定与 Survived 的相关性.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"_cell_guid\": \"725d1c84-6323-9d70-5812-baf9994d3aa1\",\n    \"_uuid\": \"572a2e9a8a268159849d89ed7ec5902a305b0d14\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>AgeBand</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>(-0.08, 16.0]</td>\\n\",\n       \"      <td>0.550000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>(16.0, 32.0]</td>\\n\",\n       \"      <td>0.337374</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>(32.0, 48.0]</td>\\n\",\n       \"      <td>0.412037</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>(48.0, 64.0]</td>\\n\",\n       \"      <td>0.434783</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>(64.0, 80.0]</td>\\n\",\n       \"      <td>0.090909</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"         AgeBand  Survived\\n\",\n       \"0  (-0.08, 16.0]  0.550000\\n\",\n       \"1   (16.0, 32.0]  0.337374\\n\",\n       \"2   (32.0, 48.0]  0.412037\\n\",\n       \"3   (48.0, 64.0]  0.434783\\n\",\n       \"4   (64.0, 80.0]  0.090909\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\\n\",\n    \"train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"ba4be3a0-e524-9c57-fbec-c8ecc5cde5c6\",\n    \"_uuid\": \"ef3552778390d811d6105a2dd0c2b81e77407283\"\n   },\n   \"source\": [\n    \"让我们使用年龄段的顺序值来替换 Aage.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"_cell_guid\": \"797b986d-2c45-a9ee-e5b5-088de817c8b2\",\n    \"_uuid\": \"f7661c3d74c059e1d3664fb502690cb9bfd27339\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"      <th>AgeBand</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>(16.0, 32.0]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>(32.0, 48.0]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>(16.0, 32.0]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>(32.0, 48.0]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>(32.0, 48.0]</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\\\\n\",\n       \"0         0       3    0    1      1      0   7.2500        S      1   \\n\",\n       \"1         1       1    1    2      1      0  71.2833        C      3   \\n\",\n       \"2         1       3    1    1      0      0   7.9250        S      2   \\n\",\n       \"3         1       1    1    2      1      0  53.1000        S      3   \\n\",\n       \"4         0       3    0    2      0      0   8.0500        S      1   \\n\",\n       \"\\n\",\n       \"        AgeBand  \\n\",\n       \"0  (16.0, 32.0]  \\n\",\n       \"1  (32.0, 48.0]  \\n\",\n       \"2  (16.0, 32.0]  \\n\",\n       \"3  (32.0, 48.0]  \\n\",\n       \"4  (32.0, 48.0]  \"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for dataset in combine:    \\n\",\n    \"    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\\n\",\n    \"    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\\n\",\n    \"    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\\n\",\n    \"    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\\n\",\n    \"    dataset.loc[ dataset['Age'] > 64, 'Age']\\n\",\n    \"train_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"004568b6-dd9a-ff89-43d5-13d4e9370b1d\",\n    \"_uuid\": \"76893d49eff6e15e05569448e030358486870409\"\n   },\n   \"source\": [\n    \"我们删除 AgeBand 特征.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"_cell_guid\": \"875e55d4-51b0-5061-b72c-8a23946133a3\",\n    \"_uuid\": \"0726fd8a5151812ae337d9ee80bd4fd1c08e592a\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\\n\",\n       \"0         0       3    0    1      1      0   7.2500        S      1\\n\",\n       \"1         1       1    1    2      1      0  71.2833        C      3\\n\",\n       \"2         1       3    1    1      0      0   7.9250        S      2\\n\",\n       \"3         1       1    1    2      1      0  53.1000        S      3\\n\",\n       \"4         0       3    0    2      0      0   8.0500        S      1\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_df = train_df.drop(['AgeBand'], axis=1)\\n\",\n    \"combine = [train_df, test_df]\\n\",\n    \"train_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"1c237b76-d7ac-098f-0156-480a838a64a9\",\n    \"_uuid\": \"9c0f835b1ce116035e125c9f33b6b529bafe5f77\"\n   },\n   \"source\": [\n    \"### 结合现有特征创建新特征\\n\",\n    \"\\n\",\n    \"我们可以为 Parch 和 SibSp 结合的 FamilySize 创建一个新的特征.\\n\",\n    \"这将使我们能够从我们的数据集中删除 Parch 和 SibSp.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"_cell_guid\": \"7e6c04ed-cfaa-3139-4378-574fd095d6ba\",\n    \"_uuid\": \"d532b86557317521f6cdfab2b05a366e8272738c\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>FamilySize</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.724138</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.578431</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.552795</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.333333</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.303538</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.200000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.136364</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   FamilySize  Survived\\n\",\n       \"3           4  0.724138\\n\",\n       \"2           3  0.578431\\n\",\n       \"1           2  0.552795\\n\",\n       \"6           7  0.333333\\n\",\n       \"0           1  0.303538\\n\",\n       \"4           5  0.200000\\n\",\n       \"5           6  0.136364\\n\",\n       \"7           8  0.000000\\n\",\n       \"8          11  0.000000\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for dataset in combine:\\n\",\n    \"    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\\n\",\n    \"\\n\",\n    \"train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"842188e6-acf8-2476-ccec-9e3451e4fa86\",\n    \"_uuid\": \"fe2ce46b514c097ac6d9b55b01ec5cca1e3cceed\"\n   },\n   \"source\": [\n    \"我们可以创建另一个名为 IsAlone 特征.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"_cell_guid\": \"5c778c69-a9ae-1b6b-44fe-a0898d07be7a\",\n    \"_uuid\": \"3251ed4ebd08403d7dab1b7a1ce7088f63f1f1b9\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>IsAlone</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.505650</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.303538</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   IsAlone  Survived\\n\",\n       \"0        0  0.505650\\n\",\n       \"1        1  0.303538\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for dataset in combine:\\n\",\n    \"    dataset['IsAlone'] = 0\\n\",\n    \"    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\\n\",\n    \"\\n\",\n    \"train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"e6b87c09-e7b2-f098-5b04-4360080d26bc\",\n    \"_uuid\": \"0eea444a8e105e416b6786fbc98b4ed6855c50f0\"\n   },\n   \"source\": [\n    \"让我们放弃 Parch, SibSp 和 FamilySize 特征, 转而使用 IsAlone 特征.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"_cell_guid\": \"74ee56a6-7357-f3bc-b605-6c41f8aa6566\",\n    \"_uuid\": \"56fadcd70a1cd2ac2110809c2279fd0b60204174\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"      <th>IsAlone</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Survived  Pclass  Sex  Age     Fare Embarked  Title  IsAlone\\n\",\n       \"0         0       3    0    1   7.2500        S      1        0\\n\",\n       \"1         1       1    1    2  71.2833        C      3        0\\n\",\n       \"2         1       3    1    1   7.9250        S      2        1\\n\",\n       \"3         1       1    1    2  53.1000        S      3        0\\n\",\n       \"4         0       3    0    2   8.0500        S      1        1\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\\n\",\n    \"test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\\n\",\n    \"combine = [train_df, test_df]\\n\",\n    \"\\n\",\n    \"train_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"f890b730-b1fe-919e-fb07-352fbd7edd44\",\n    \"_uuid\": \"512eac83ac2ba3117b275e48518596352a4ca0ff\"\n   },\n   \"source\": [\n    \"我们还可以创建一个结合 Pclass 和 Age 的人造特征.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"_cell_guid\": \"305402aa-1ea1-c245-c367-056eef8fe453\",\n    \"_uuid\": \"d8ec9d2b6d57f680a248275d032a1735f2ee5f1a\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Age*Class</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Age*Class  Age  Pclass\\n\",\n       \"0          3    1       3\\n\",\n       \"1          2    2       1\\n\",\n       \"2          3    1       3\\n\",\n       \"3          2    2       1\\n\",\n       \"4          6    2       3\\n\",\n       \"5          3    1       3\\n\",\n       \"6          3    3       1\\n\",\n       \"7          0    0       3\\n\",\n       \"8          3    1       3\\n\",\n       \"9          0    0       2\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for dataset in combine:\\n\",\n    \"    dataset['Age*Class'] = dataset.Age * dataset.Pclass\\n\",\n    \"\\n\",\n    \"train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"13292c1b-020d-d9aa-525c-941331bb996a\",\n    \"_uuid\": \"9ffa98cb5aec630439e16831382f8456e0db4ce7\"\n   },\n   \"source\": [\n    \"### 完整化分类特征\\n\",\n    \"\\n\",\n    \"Embarked（出发港）特征有 S, Q, C 三个基于出发港口的值.\\n\",\n    \"我们的训练集有两个丢失值.\\n\",\n    \"我们简单的使用最常发生的情况来填充它.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"_cell_guid\": \"bf351113-9b7f-ef56-7211-e8dd00665b18\",\n    \"_uuid\": \"9c05cd517bc2727bfba3b88fc283f9b2f59cb51f\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'S'\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"freq_port = train_df.Embarked.dropna().mode()[0]\\n\",\n    \"freq_port\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"_cell_guid\": \"51c21fcc-f066-cd80-18c8-3d140be6cbae\",\n    \"_uuid\": \"f820e03ca066443667a164be135a2d2217958d75\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>0.553571</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"      <td>0.389610</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0.339009</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"  Embarked  Survived\\n\",\n       \"0        C  0.553571\\n\",\n       \"1        Q  0.389610\\n\",\n       \"2        S  0.339009\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for dataset in combine:\\n\",\n    \"    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\\n\",\n    \"    \\n\",\n    \"train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"f6acf7b2-0db3-e583-de50-7e14b495de34\",\n    \"_uuid\": \"b683a829d3a58ef016d38ba2146931c87241e4ec\"\n   },\n   \"source\": [\n    \"### 转换分类特征为数值的\\n\",\n    \"\\n\",\n    \"我们现在可以通过创建一个新的数字港特征来转换 EmbarkedFill 特征.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"_cell_guid\": \"89a91d76-2cc0-9bbb-c5c5-3c9ecae33c66\",\n    \"_uuid\": \"c9378a196d37cf745a6989f7de3bfec5dab80b35\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"      <th>IsAlone</th>\\n\",\n       \"      <th>Age*Class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Survived  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\\n\",\n       \"0         0       3    0    1   7.2500         0      1        0          3\\n\",\n       \"1         1       1    1    2  71.2833         1      3        0          2\\n\",\n       \"2         1       3    1    1   7.9250         0      2        1          3\\n\",\n       \"3         1       1    1    2  53.1000         0      3        0          2\\n\",\n       \"4         0       3    0    2   8.0500         0      1        1          6\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for dataset in combine:\\n\",\n    \"    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\\n\",\n    \"\\n\",\n    \"train_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"e3dfc817-e1c1-a274-a111-62c1c814cecf\",\n    \"_uuid\": \"b6f1a6648c4cfcce63bba886403bae239413b912\"\n   },\n   \"source\": [\n    \"### 快速完整化兵转换数值的特征\\n\",\n    \"\\n\",\n    \"现在，我们可以在测试数据集使用模式下为单个缺失值完整化票价特征, 以获取此特征最常出现的值. 我们用一行代码来完成.\\n\",\n    \"\\n\",\n    \"请注意, 我们并没有创建中间用的新特征, 也没有对相关性进行任何进一步的分析以猜测丢失的特征, 因为我们只替换单个值. 完成目标达到了模型算法对非空值操作的期望要求.\\n\",\n    \"\\n\",\n    \"我们可能还想把票价四舍五入到小数点后两位, 因为它代表货币.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"_cell_guid\": \"3600cb86-cf5f-d87b-1b33-638dc8db1564\",\n    \"_uuid\": \"b697a634d61798fe5b64d128b330eaae7e219eb2\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"      <th>IsAlone</th>\\n\",\n       \"      <th>Age*Class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>892</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>7.8292</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>893</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>7.0000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>894</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>9.6875</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>895</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>8.6625</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>896</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>12.2875</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\\n\",\n       \"0          892       3    0    2   7.8292         2      1        1          6\\n\",\n       \"1          893       3    1    2   7.0000         0      3        0          6\\n\",\n       \"2          894       2    0    3   9.6875         2      1        1          6\\n\",\n       \"3          895       3    0    1   8.6625         0      1        1          3\\n\",\n       \"4          896       3    1    1  12.2875         0      3        0          3\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\\n\",\n    \"test_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"4b816bc7-d1fb-c02b-ed1d-ee34b819497d\",\n    \"_uuid\": \"8ae1f2214210644f0670e9d16e4ab19a5c1e9f36\"\n   },\n   \"source\": [\n    \"我们创建 FareBand 特征.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"_cell_guid\": \"0e9018b1-ced5-9999-8ce1-258a0952cbf2\",\n    \"_uuid\": \"098b3896b60d1385b2297c16c405d33778e44993\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>FareBand</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>(-0.001, 7.91]</td>\\n\",\n       \"      <td>0.197309</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>(7.91, 14.454]</td>\\n\",\n       \"      <td>0.303571</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>(14.454, 31.0]</td>\\n\",\n       \"      <td>0.454955</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>(31.0, 512.329]</td>\\n\",\n       \"      <td>0.581081</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"          FareBand  Survived\\n\",\n       \"0   (-0.001, 7.91]  0.197309\\n\",\n       \"1   (7.91, 14.454]  0.303571\\n\",\n       \"2   (14.454, 31.0]  0.454955\\n\",\n       \"3  (31.0, 512.329]  0.581081\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\\n\",\n    \"train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"d65901a5-3684-6869-e904-5f1a7cce8a6d\",\n    \"_uuid\": \"c6d38425e429ad5f7370174aee7db5fc28b9d0c1\"\n   },\n   \"source\": [\n    \"将 Fare 特征转换为基于 FareBand 的顺序值.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"_cell_guid\": \"385f217a-4e00-76dc-1570-1de4eec0c29c\",\n    \"_uuid\": \"bc6ba15392d78699e3ed46cf539d1251d3d64ec2\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"      <th>IsAlone</th>\\n\",\n       \"      <th>Age*Class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\\n\",\n       \"0         0       3    0    1     0         0      1        0          3\\n\",\n       \"1         1       1    1    2     3         1      3        0          2\\n\",\n       \"2         1       3    1    1     1         0      2        1          3\\n\",\n       \"3         1       1    1    2     3         0      3        0          2\\n\",\n       \"4         0       3    0    2     1         0      1        1          6\\n\",\n       \"5         0       3    0    1     1         2      1        1          3\\n\",\n       \"6         0       1    0    3     3         0      1        1          3\\n\",\n       \"7         0       3    0    0     2         0      4        0          0\\n\",\n       \"8         1       3    1    1     1         0      3        0          3\\n\",\n       \"9         1       2    1    0     2         1      3        0          0\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for dataset in combine:\\n\",\n    \"    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\\n\",\n    \"    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\\n\",\n    \"    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\\n\",\n    \"    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\\n\",\n    \"    dataset['Fare'] = dataset['Fare'].astype(int)\\n\",\n    \"\\n\",\n    \"train_df = train_df.drop(['FareBand'], axis=1)\\n\",\n    \"combine = [train_df, test_df]\\n\",\n    \"    \\n\",\n    \"train_df.head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"27272bb9-3c64-4f9a-4a3b-54f02e1c8289\",\n    \"_uuid\": \"daa1ef2745944d7029c5c1d4659c1e393f19013b\"\n   },\n   \"source\": [\n    \"并且测试数据集也一样.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"_cell_guid\": \"d2334d33-4fe5-964d-beac-6aa620066e15\",\n    \"_uuid\": \"550dccae1471b9c2f6d627ae48d376ca59a322c1\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"      <th>IsAlone</th>\\n\",\n       \"      <th>Age*Class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>892</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>893</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>894</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>895</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>896</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>897</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>898</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>899</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>900</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>901</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\\n\",\n       \"0          892       3    0    2     0         2      1        1          6\\n\",\n       \"1          893       3    1    2     0         0      3        0          6\\n\",\n       \"2          894       2    0    3     1         2      1        1          6\\n\",\n       \"3          895       3    0    1     1         0      1        1          3\\n\",\n       \"4          896       3    1    1     1         0      3        0          3\\n\",\n       \"5          897       3    0    0     1         0      1        1          0\\n\",\n       \"6          898       3    1    1     0         2      2        1          3\\n\",\n       \"7          899       2    0    1     2         0      1        0          2\\n\",\n       \"8          900       3    1    1     0         1      3        1          3\\n\",\n       \"9          901       3    0    1     2         0      1        0          3\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_df.head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"69783c08-c8cc-a6ca-2a9a-5e75581c6d31\",\n    \"_uuid\": \"d5bf81957541c7ce9dcb2f25210f1bb9fe2f0602\"\n   },\n   \"source\": [\n    \"## 模型, 预测和解决方案\\n\",\n    \"\\n\",\n    \"现在我们准备训练模型并通过训练得到的模型预测结果。有60多种用于预测的模型可供选择。我们必须了解问题的类型和解决方案的要求，将模型数量缩小到少数几个。我们的问题是分类和回归问题，因为需要确定输出（生存与否）与其他变量或特征（性别，年龄，港口...）之间的关系。此外，我们的问题应该属于监督学习，因为我们用已知类别的数据集来训练我们的模型。有了监督学习、分类和回归这两个标准，我们可以将模型选择的范围缩小到几个。这些包括：\\n\",\n    \"- Logistic回归\\n\",\n    \"- KNN或K—近邻\\n\",\n    \"- 支持向量机\\n\",\n    \"- 朴素贝叶斯分类器\\n\",\n    \"- 决策树\\n\",\n    \"- 随机森林\\n\",\n    \"- 感知器\\n\",\n    \"- 人工神经网络\\n\",\n    \"- 相关向量机\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {\n    \"_cell_guid\": \"0acf54f9-6cf5-24b5-72d9-29b30052823a\",\n    \"_uuid\": \"2ba5475a0389b56ceb1ddcb5b1ed927107e80fe8\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"((891, 8), (891,), (418, 8))\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_train = train_df.drop(\\\"Survived\\\", axis=1)\\n\",\n    \"Y_train = train_df[\\\"Survived\\\"]\\n\",\n    \"X_test  = test_df.drop(\\\"PassengerId\\\", axis=1).copy()\\n\",\n    \"X_train.shape, Y_train.shape, X_test.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"579bc004-926a-bcfe-e9bb-c8df83356876\",\n    \"_uuid\": \"dcd0657cf810fe62e145a86ba7cdc5c1f7370e7a\"\n   },\n   \"source\": [\n    \"Logistic回归形式简单，易于建模，适合用于早期的工作流程。Logistics回归使用线性回归模型的预测结果去逼近真实标记的对数几率，形式为参数化的Logistics分布。参考维基百科[Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression).\\n\",\n    \"\\n\",\n    \"注意模型产生的“置信度评分”是基于训练集的。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {\n    \"_cell_guid\": \"0edd9322-db0b-9c37-172d-a3a4f8dec229\",\n    \"_uuid\": \"f0b92b7d35145c43a11ef16d4be0d257c616fb37\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"80.359999999999999\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Logistic Regression\\n\",\n    \"\\n\",\n    \"logreg = LogisticRegression()\\n\",\n    \"logreg.fit(X_train, Y_train)\\n\",\n    \"Y_pred = logreg.predict(X_test)\\n\",\n    \"acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\\n\",\n    \"acc_log\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"3af439ae-1f04-9236-cdc2-ec8170a0d4ee\",\n    \"_uuid\": \"461f8f4d266fb785bd3f29fa0aa9fd47353a4053\"\n   },\n   \"source\": [\n    \"我们可以使用Logistic回归来验证我们之前对特征的创建所做的假设。这可以通过计算决策函数中的特征的系数来完成。\\n\",\n    \"\\n\",\n    \"系数为正说明该特征增加了结果的对数几率（因而增加了概率），系数为负说明该特征降低了结果的对数几率（从而降低了概率）\\n\",\n    \"\\n\",\n    \"- Sex特征有最高的正系数，意味着当Sex从男（0）变成女（1）时，Survived = 1的概率增加最多。\\n\",\n    \"- 相反地，随着Pclass特征的增加，Survived = 1的概率减少的最多。\\n\",\n    \"- Age * Class是一个很好的人造特征，因为它与Survived具有次高的负相关性。\\n\",\n    \"- Title特征有第二高的正相关系数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"_cell_guid\": \"e545d5aa-4767-7a41-5799-a4c5e529ce72\",\n    \"_uuid\": \"d13fa4e9617cc61c8801689fd4d9e5470510d7ad\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Feature</th>\\n\",\n       \"      <th>Correlation</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>Sex</td>\\n\",\n       \"      <td>2.201527</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>Title</td>\\n\",\n       \"      <td>0.398234</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>Age</td>\\n\",\n       \"      <td>0.287164</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>Embarked</td>\\n\",\n       \"      <td>0.261762</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>IsAlone</td>\\n\",\n       \"      <td>0.129140</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>Fare</td>\\n\",\n       \"      <td>-0.085150</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>Age*Class</td>\\n\",\n       \"      <td>-0.311199</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>Pclass</td>\\n\",\n       \"      <td>-0.749006</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"     Feature  Correlation\\n\",\n       \"1        Sex     2.201527\\n\",\n       \"5      Title     0.398234\\n\",\n       \"2        Age     0.287164\\n\",\n       \"4   Embarked     0.261762\\n\",\n       \"6    IsAlone     0.129140\\n\",\n       \"3       Fare    -0.085150\\n\",\n       \"7  Age*Class    -0.311199\\n\",\n       \"0     Pclass    -0.749006\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"coeff_df = pd.DataFrame(train_df.columns.delete(0))\\n\",\n    \"coeff_df.columns = ['Feature']\\n\",\n    \"coeff_df[\\\"Correlation\\\"] = pd.Series(logreg.coef_[0])\\n\",\n    \"\\n\",\n    \"coeff_df.sort_values(by='Correlation', ascending=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"ac041064-1693-8584-156b-66674117e4d0\",\n    \"_uuid\": \"07a0a0f3a820c9d4ca0472d1b9ec05fa822d3479\"\n   },\n   \"source\": [\n    \"接下来，我们使用支持向量机（SVM）模型。支持向量机是一个监督学习模型，它使用相关学习算法来分析数据，可以用于分类和回归问题。在二元分类的情况下，SVM算法建立一个模型，去找两类训练样本“正中间”的划分超平面，因为该划分超平面对训练样本局部扰动的“容忍性”最好。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Support_vector_machine).\\n\",\n    \"\\n\",\n    \"注意SVM模型生成的“置信度评分”高于Logistics回归模型。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"_cell_guid\": \"7a63bf04-a410-9c81-5310-bdef7963298f\",\n    \"_uuid\": \"32a425989ee9cf681fad51c867ce3c76126f5b05\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"83.840000000000003\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Support Vector Machines\\n\",\n    \"\\n\",\n    \"svc = SVC()\\n\",\n    \"svc.fit(X_train, Y_train)\\n\",\n    \"Y_pred = svc.predict(X_test)\\n\",\n    \"acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\\n\",\n    \"acc_svc\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"172a6286-d495-5ac4-1a9c-5b77b74ca6d2\",\n    \"_uuid\": \"0075b5fb532a249c701efa7ef84b2f52c9f29776\"\n   },\n   \"source\": [\n    \"在模式识别中，k-近邻算法（简称k-NN）是一种用于分类和回归的无参数方法。测试样本找出训练集中与其最靠近的k个训练样本，选择这k个样本中出现最多的类别标记作为预测结果（k是一个正整数，通常很小）。如果k = 1，则该对象的类别和最近邻样本的类别一致。 参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm).\\n\",\n    \"\\n\",\n    \"KNN的“置信度评分”比Logistics回归好，但比SVM差。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"_cell_guid\": \"ca14ae53-f05e-eb73-201c-064d7c3ed610\",\n    \"_uuid\": \"f65598719a7e411ec09f6665d98f86e3e26a1f85\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"84.739999999999995\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"knn = KNeighborsClassifier(n_neighbors = 3)\\n\",\n    \"knn.fit(X_train, Y_train)\\n\",\n    \"Y_pred = knn.predict(X_test)\\n\",\n    \"acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\\n\",\n    \"acc_knn\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"810f723d-2313-8dfd-e3e2-26673b9caa90\",\n    \"_uuid\": \"c1e80aa85d47f1076aa3d0628a37d903b1959ad4\"\n   },\n   \"source\": [\n    \"在机器学习中，朴素贝叶斯分类器是一个基于所有特征互相独立的贝叶斯理论的简单概率分类器。朴素贝叶斯分类器具有高度可扩展性，在学习过程中需要大量的线性特征作为参数。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier).\\n\",\n    \"\\n\",\n    \"该模型生成的“置信度评分”是目前模型中最低的。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {\n    \"_cell_guid\": \"50378071-7043-ed8d-a782-70c947520dae\",\n    \"_uuid\": \"db060b792effa86c5483bf02786b8a69bb043fd5\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"72.280000000000001\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Gaussian Naive Bayes\\n\",\n    \"\\n\",\n    \"gaussian = GaussianNB()\\n\",\n    \"gaussian.fit(X_train, Y_train)\\n\",\n    \"Y_pred = gaussian.predict(X_test)\\n\",\n    \"acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\\n\",\n    \"acc_gaussian\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"1e286e19-b714-385a-fcfa-8cf5ec19956a\",\n    \"_uuid\": \"c5f397f24dda3a6181708bee43314f6f316d1328\"\n   },\n   \"source\": [\n    \"感知器是用于二元分类器的监督学习的算法（可以决定包含一个向量的输入是否属于某个类别）。它是一种线性分类器，即一种分类算法，通过一个线性预测函数将一组权重与特征向量组合来进行预测。该算法允许在线学习，因为它在一次迭代中只处理一个训练集中的元素。 参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Perceptron).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"_cell_guid\": \"ccc22a86-b7cb-c2dd-74bd-53b218d6ed0d\",\n    \"_uuid\": \"4ae3698170341015098b1fcc4b716bbee4b01f54\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"D:\\\\Anaconda\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.perceptron.Perceptron'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\\n\",\n      \"  \\\"and default tol will be 1e-3.\\\" % type(self), FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"78.0\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Perceptron\\n\",\n    \"\\n\",\n    \"perceptron = Perceptron()\\n\",\n    \"perceptron.fit(X_train, Y_train)\\n\",\n    \"Y_pred = perceptron.predict(X_test)\\n\",\n    \"acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\\n\",\n    \"acc_perceptron\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {\n    \"_cell_guid\": \"a4d56857-9432-55bb-14c0-52ebeb64d198\",\n    \"_uuid\": \"a7667ed1e8c4f753d0c8a04111beee9526f3e0e6\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"79.120000000000005\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Linear SVC\\n\",\n    \"\\n\",\n    \"linear_svc = LinearSVC()\\n\",\n    \"linear_svc.fit(X_train, Y_train)\\n\",\n    \"Y_pred = linear_svc.predict(X_test)\\n\",\n    \"acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\\n\",\n    \"acc_linear_svc\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {\n    \"_cell_guid\": \"dc98ed72-3aeb-861f-804d-b6e3d178bf4b\",\n    \"_uuid\": \"4d0cc9dd1855a0e8c2206a7ae53f2aa5c35b87b3\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"D:\\\\Anaconda\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\\n\",\n      \"  \\\"and default tol will be 1e-3.\\\" % type(self), FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"80.019999999999996\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Stochastic Gradient Descent\\n\",\n    \"\\n\",\n    \"sgd = SGDClassifier()\\n\",\n    \"sgd.fit(X_train, Y_train)\\n\",\n    \"Y_pred = sgd.predict(X_test)\\n\",\n    \"acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\\n\",\n    \"acc_sgd\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"bae7f8d7-9da0-f4fd-bdb1-d97e719a18d7\",\n    \"_uuid\": \"5e191ae0e5c2fad6c4601d792cbc3d7b71097822\"\n   },\n   \"source\": [\n    \"该模型使用决策树作为预测模型，将特征（树的分支）映射到决策结果（树的叶结点）。目标变量是有限的一组值的树称为分类树; 在这些树结构中，叶结点对应于决策结果，其他每个结点对应于一个属性测试，每个结点包含的样本集合根据属性测试的结果被划分到子结点中。目标变量可以取连续值（通常是实数）的决策树称为回归树。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning).\\n\",\n    \"\\n\",\n    \"该模型的“置信度评分”是目前模型中最高的。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"_cell_guid\": \"dd85f2b7-ace2-0306-b4ec-79c68cd3fea0\",\n    \"_uuid\": \"acc5910f9900f1404b6fcdba93dd28fdda0766b7\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"86.760000000000005\"\n      ]\n     },\n     \"execution_count\": 48,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Decision Tree\\n\",\n    \"\\n\",\n    \"decision_tree = DecisionTreeClassifier()\\n\",\n    \"decision_tree.fit(X_train, Y_train)\\n\",\n    \"Y_pred = decision_tree.predict(X_test)\\n\",\n    \"acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\\n\",\n    \"acc_decision_tree\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"85693668-0cd5-4319-7768-eddb62d2b7d0\",\n    \"_uuid\": \"0c37a62dd5b0c6e9a6f644d45a92eb3851bc2991\"\n   },\n   \"source\": [\n    \"随机森林是最流行的模型之一。随机森林或随机决策树森林是一种用于分类，回归或其他任务的集成学习模型，它通过在训练时构造大量的决策树（n_estimators = 100），再使用某种策略将这些“个体学习器”结合起来。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Random_forest).\\n\",\n    \"\\n\",\n    \"该模型的“置信度评分”是目前模型中最高的。我们决定使用这个模型的输出（Y_pred）来作为竞赛结果。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {\n    \"_cell_guid\": \"f0694a8e-b618-8ed9-6f0d-8c6fba2c4567\",\n    \"_uuid\": \"a3a92337489f7f75e233d7a5d645cdbf791071e7\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"86.760000000000005\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Random Forest\\n\",\n    \"\\n\",\n    \"random_forest = RandomForestClassifier(n_estimators=100)\\n\",\n    \"random_forest.fit(X_train, Y_train)\\n\",\n    \"Y_pred = random_forest.predict(X_test)\\n\",\n    \"random_forest.score(X_train, Y_train)\\n\",\n    \"acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\\n\",\n    \"acc_random_forest\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"f6c9eef8-83dd-581c-2d8e-ce932fe3a44d\",\n    \"_uuid\": \"24d40c30b491d0d109908035ed86f0929860dd5e\"\n   },\n   \"source\": [\n    \"### 模型评估\\n\",\n    \"\\n\",\n    \"现在, 我们可以对所有模型进行评估, 为我们的问题选择最好的模型。\\n\",\n    \"虽然决策树和随机森林评分相同, 但我们选择使用随机森林，因为随机森林会校正决策树“过拟合”的缺点。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {\n    \"_cell_guid\": \"1f3cebe0-31af-70b2-1ce4-0fd406bcdfc6\",\n    \"_uuid\": \"79536b2878fa12ceaf3648bfd5bb5de63b903709\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Model</th>\\n\",\n       \"      <th>Score</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>Random Forest</td>\\n\",\n       \"      <td>86.76</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>Decision Tree</td>\\n\",\n       \"      <td>86.76</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>KNN</td>\\n\",\n       \"      <td>84.74</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>Support Vector Machines</td>\\n\",\n       \"      <td>83.84</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>Logistic Regression</td>\\n\",\n       \"      <td>80.36</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>Stochastic Gradient Decent</td>\\n\",\n       \"      <td>80.02</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>Linear SVC</td>\\n\",\n       \"      <td>79.12</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>Perceptron</td>\\n\",\n       \"      <td>78.00</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>Naive Bayes</td>\\n\",\n       \"      <td>72.28</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                        Model  Score\\n\",\n       \"3               Random Forest  86.76\\n\",\n       \"8               Decision Tree  86.76\\n\",\n       \"1                         KNN  84.74\\n\",\n       \"0     Support Vector Machines  83.84\\n\",\n       \"2         Logistic Regression  80.36\\n\",\n       \"6  Stochastic Gradient Decent  80.02\\n\",\n       \"7                  Linear SVC  79.12\\n\",\n       \"5                  Perceptron  78.00\\n\",\n       \"4                 Naive Bayes  72.28\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"models = pd.DataFrame({\\n\",\n    \"    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \\n\",\n    \"              'Random Forest', 'Naive Bayes', 'Perceptron', \\n\",\n    \"              'Stochastic Gradient Decent', 'Linear SVC', \\n\",\n    \"              'Decision Tree'],\\n\",\n    \"    'Score': [acc_svc, acc_knn, acc_log, \\n\",\n    \"              acc_random_forest, acc_gaussian, acc_perceptron, \\n\",\n    \"              acc_sgd, acc_linear_svc, acc_decision_tree]})\\n\",\n    \"models.sort_values(by='Score', ascending=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"_cell_guid\": \"28854d36-051f-3ef0-5535-fa5ba6a9bef7\",\n    \"_uuid\": \"a2cda3bdd06c9b6a0cb2c02ca276c049865108fb\",\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"submission = pd.DataFrame({\\n\",\n    \"        \\\"PassengerId\\\": test_df[\\\"PassengerId\\\"],\\n\",\n    \"        \\\"Survived\\\": Y_pred\\n\",\n    \"    })\\n\",\n    \"# submission.to_csv('../output/submission.csv', index=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"fcfc8d9f-e955-cf70-5843-1fb764c54699\",\n    \"_uuid\": \"b8e1264e98af00d119e07a776643e6ce08b59666\"\n   },\n   \"source\": [\n    \"我们提交给竞赛网站 Kaggle 的比赛结果在 6,082 个参赛作品中获得 3883 名.\\n\",\n    \"当竞赛正在进行时，这个结果是具有指导意义的.\\n\",\n    \"这个结果只占提交数据集的一部分.\\n\",\n    \"对我们的第一次尝试是不错的.\\n\",\n    \"欢迎任何提高我们的分数的建议.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"aeec9210-f9d8-cd7c-c4cf-a87376d5f693\",\n    \"_uuid\": \"b4758c6c3b2f72da72397cf2f82bc0b92bec3c5b\"\n   },\n   \"source\": [\n    \"## 参考文献\\n\",\n    \"\\n\",\n    \"该手册是基于完成解决《泰坦尼克号》竞赛和其它来源的伟大工作而创建的.\\n\",\n    \"\\n\",\n    \"- [泰坦尼克号之旅](https://www.kaggle.com/omarelgabry/titanic/a-journey-through-titanic)\\n\",\n    \"- [ Pandas 入门指南: Kaggle 的泰坦尼克号竞赛](https://www.kaggle.com/c/titanic/details/getting-started-with-random-forests)\\n\",\n    \"- [泰坦尼克号的最佳处理分类器](https://www.kaggle.com/sinakhorami/titanic/titanic-best-working-classifier)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"_change_revision\": 0,\n  \"_is_fork\": false,\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/titanic/titanic-data-science-solutions.md",
    "content": "\n# 《泰坦尼克号》数据科学解决方案\n\n## 翻译相关\n原文地址: <https://www.kaggle.com/startupsci/titanic-data-science-solutions?scriptVersionId=1145136>  \n开源组织: [ApacheCN ~ apachecn.org](http://www.apachecn.org)   \n贡献者: [@那伊抹微笑](https://github.com/wangyangting), [@李铭哲](https://github.com/limingzhe), [@刘海飞](https://github.com/WindZQ), [@DL - 小王子](https://github.com/VPrincekin), [@成飘飘](https://github.com/chengpiaopiao)  \n最近更新: 2018-01-03\n\n---\n\n### 我已经发布了一个新的 Python 包 [Speedml](https://speedml.com), 它将该 notebook 中的使用的技术编译成一个 intuitive（直观的），powerful（功能强大的）且 productive（高效的）API.\n\n### Speedml 帮助我在 Kaggle 排行榜上从最低的 80% 跳到最高的 20%, 迭代的次数很少.\n\n### 还有一件事...Speedml 实现了这一点, 代码行数减少了近 70%!\n\n### 下载并且运行代码 [Speedml 版本的泰坦尼克号解决方案](https://github.com/Speedml/notebooks/blob/master/titanic/titanic-solution-using-speedml.ipynb).\n\n---\n\n该 notebook 是 [Data Science Solutions](https://startupsci.com) 书籍的一个手册. 该 notebook 引导我们通过一个典型的工作流程来解决像 Kaggle 这样类似的网站的数据科学竞赛.\n\n有几个优秀的 notebooks 可以用来研究数据科学竞赛作品.\n然而许多手册将会跳过一些关于如何开发解决方案的解释, 因为这些 notebooks 是专门为这些专家开发的.\n该 notebook 的目标是遵循一步一步的工作流程, 解释我们在解决方案开发过程中所做的每一个决策的每个步骤和理由.\n\n## 工作流阶段\n\n1. 问题或问题的定义.\n2. 获取 training（训练）和 testing（测试）数据.\n3. Wrangle（整理）, prepare（准备）, cleanse（清洗）数据\n4. Analyze（分析）, identify patterns 以及探索数据.\n5. Model（模型）, predict（预测）以及解决问题.\n6. Visualize（可视化）, report（报告）和提出解决问题的步骤以及最终解决方案.\n7. 提供或提交结果.\n\n该工作流指出了，每个阶段如何遵循另一个阶段的常见顺序.\n但是也有例外的场景.\n\n- 我们可能结合多个工作流阶段. 我们可以通过可视化数据进行分析.\n- 比 indicated（说明）更早的进行一个阶段. 我们可能在 wrangling（整理）过程的前后来分析数据.\n- 在我们的工作流程中多次执行一个阶段. 可视化阶段可能被使用多次.\n- Drop a stage altogether. We may not need supply stage to productize or service enable our dataset for a competition.\n\n\n## 问题和问题定义\n\n像 Kaggle 这样的竞赛网站, 它们会定义要解决或质疑的问题, 同时提供用于训练数据科学模型和根据测试数据集测试模型结果的数据集,（即, 训练集 和 测试集）.\n针对《泰坦尼克号生存竞赛》的问题或定义在 [这里是 Kaggle 描述](https://www.kaggle.com/c/titanic) 中有描述.\n\n> 从泰坦尼克号的灾难中幸存下来或没有幸存的乘客的样本训练集（train.csv）中，如果测试数据集（test.csv）中的这些乘客幸存下来，我们的模型是否可以基于给定的测试数据集（test.csv）来确定。\n\n我们也可能希望对我们问题的领域有所了解.\n这在 [Kaggle 竞赛描述](https://www.kaggle.com/c/titanic) 页面有详细的描述.\n以下是要注意的事项.\n\n- 1912年4月15日, 在首航期间, 泰坦尼克号撞上一座冰山后沉没, 2224 名乘客和机组人员中有 1502 人遇难. 生成率解释为 32%.\n- 还难导致生命损失的原因之一是没有足够的救生艇给乘客和船员.\n- 尽管幸存下来的运气有一些因素, 但一些人比其他人更有可能幸存下来，比如妇女, 儿童和上层阶级.\n\n## 工作流目标\n\n数据科学解决方案工作流程有以下七个主要的目标.\n\n**Classifying（分类）.** 我们可能想对我们的样本进行分类或加以类别. 我们也可能想要了解不同类别与解决方案目标的含义或相关性.\n\n**Correlating（相关）.** 可以根据训练数据集中的可用特征来处理这个问题. 数据集中的哪些特征对我们的解决方案目标有重大贡献？从统计学上讲, 特征和解决方案的目标中有一个[相关](https://en.wikiversity.org/wiki/Correlation)？随着特征值的改变, 解决方案的状态也会随之改变, 反之亦然？这可以针对给定数据集中的数字和分类特征进行测试. 我们也可能想要确定以后的目标和工作流程阶段的生存以外的特征之间的相关性. 关联某些特征可能有助于创建, 完善或纠正特征。\n\n**Converting（转换）.** 对于建模阶段, 需要准备数据. 根据模型算法的选择, 可能需要将所有特征转换为数值等价值. 所以例如将文本分类值转换为数字的值.\n\n**Completing（完整）.** 数据准备也可能要求我们估计一个特征中的任何缺失值. 当没有缺失值时，模型算法可能效果最好.\n\n**Correcting（校正）.** 我们还可以分析给定的训练数据集以找出错误或者可能在特征内不准确的值, 并尝试对这些值进行校正或排除包含错误的样本. 一种方法是检测样本或特征中的任何异常值. 如果对分析没有贡献, 或者可能会显着扭曲结果, 我们也可能完全丢弃一个特征.\n\n**Creating（创建）.** 我们可以根据现有特征或一组特征来创建新特征, 以便新特征遵循 correlation（相关）, conversion（转换）, completeness（完整）的目标.\n\n**Charting（绘图）.** 如何根据数据的性质和解决方案的目标来选择正确的可视化图表工具以及绘图.\n\n## 重构的发布日期 2017年1月29日\n\nWe are significantly refactoring the notebook based on (a) comments received by readers, (b) issues in porting notebook from Jupyter kernel (2.7) to Kaggle kernel (3.5), and (c) review of few more best practice kernels.\n\n### 用户评论\n\n- Combine training and test data for certain operations like converting titles across dataset to numerical values. (thanks @Sharan Naribole)\n- Correct observation - nearly 30% of the passengers had siblings and/or spouses aboard. (thanks @Reinhard)\n- Correctly interpreting logistic regresssion coefficients. (thanks @Reinhard)\n\n### 移植问题\n\n- Specify plot dimensions, bring legend into plot.\n\n\n### 最佳实践\n\n- 在项目早期进行特征相关分析.\n- 为了可读性, 使用多个图而不是覆盖图.\n\n\n```python\n# 数据分析和整理\nimport pandas as pd\nimport numpy as np\nimport random as rnd\n\n# 可视化\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\n# 机器学习\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC, LinearSVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import Perceptron\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.tree import DecisionTreeClassifier\n```\n\n## 获取数据\n\nPython 的 Pandas 包帮助我们处理我们的数据集.\n我们首先将训练和测试数据集收集到 Pandas DataFrame 中.\n我们还将这些数据集组合在一起, 在两个数据集上运行某些操作.\n\n\n```python\ntrain_df = pd.read_csv('../input/train.csv')\ntest_df = pd.read_csv('../input/test.csv')\ncombine = [train_df, test_df]\n```\n\n## 通过 describing（描述）数据进行分析\n\n在我们的项目早期, Pandas 还帮助描述回答数据集中的以下问题.\n\n**数据集中哪些特征是可用的?**\n\n注意: 直接操作或分析这些特征的名称.\n这些特征名称在 [Kaggle 数据页面](https://www.kaggle.com/c/titanic/data) 页面上有描述.\n\n\n```python\nprint(train_df.columns.values)\n```\n\n    ['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n     'Ticket' 'Fare' 'Cabin' 'Embarked']\n\n\n**哪些特征是 categorical（分类的）?**\n\n这些值将样本分成几组相似的样本.\n在分类特征中的值是 nominal（标称的）, ordinal（顺序的）或 ratio（比例的）还是 interval based（基于区间的）值？\n除此之外, 这有助于我们选择合适的图表进行可视化.\n\n- Categorical（分类的）: Survived, Sex, and Embarked. Ordinal（顺序的）: Pclass.\n\n**哪些特征是 numerical（数值的）?**\n\n哪些特征是数值的？\n这些值随样本而变化.\n在数值特征中的值是 discrete（离散的）和 continuous（连续的） 还是 timeseries based（基于时间序列的）？\n\n- Continous（连续的）: Age, Fare. Discrete（离散的）: SibSp, Parch.\n\n\n```python\n# 预览数据\ntrain_df.head()\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Braund, Mr. Owen Harris</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>A/5 21171</td>\n      <td>7.2500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>PC 17599</td>\n      <td>71.2833</td>\n      <td>C85</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>Heikkinen, Miss. Laina</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>STON/O2. 3101282</td>\n      <td>7.9250</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113803</td>\n      <td>53.1000</td>\n      <td>C123</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Allen, Mr. William Henry</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>373450</td>\n      <td>8.0500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n**哪些特征是混合的数据类型?**\n\n相同特征中的 numerical（数值的）, alphanumeric（字母数值的）.\n这些是校正目标的候选特征.\n\n- Ticket 是numerical（数值的）和 alphanumeric（字母数值的）数据类型的混合类型. Cabin 是 alphanumeric（字母数值的）.\n\n**哪些特征也许包含错误或拼写错误?**\n\n对于一个大型的数据集来说, 这是很难审查的, 但是从较小的数据集中查看一些样本可能会直接告诉我们, 哪些特征可能需要校正.\n\n- Name 特征也许包含错误或拼写错误, 因为有几种方法可以用来描述名称, 包括头衔，圆括号和用于替代或短名称的引号.\n\n\n```python\ntrain_df.tail()\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>886</th>\n      <td>887</td>\n      <td>0</td>\n      <td>2</td>\n      <td>Montvila, Rev. Juozas</td>\n      <td>male</td>\n      <td>27.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>211536</td>\n      <td>13.00</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>887</th>\n      <td>888</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Graham, Miss. Margaret Edith</td>\n      <td>female</td>\n      <td>19.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>112053</td>\n      <td>30.00</td>\n      <td>B42</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>888</th>\n      <td>889</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n      <td>female</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>2</td>\n      <td>W./C. 6607</td>\n      <td>23.45</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>889</th>\n      <td>890</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Behr, Mr. Karl Howell</td>\n      <td>male</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>111369</td>\n      <td>30.00</td>\n      <td>C148</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>890</th>\n      <td>891</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Dooley, Mr. Patrick</td>\n      <td>male</td>\n      <td>32.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>370376</td>\n      <td>7.75</td>\n      <td>NaN</td>\n      <td>Q</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n**哪些特征包含 blank（空格）, null（无效的）或 empty values（空值）?**\n\n这些将需要校正.\n\n- Cabin > Age > Embarked features contain a number of null values in that order for the training dataset.\n- Cabin > Age are incomplete in case of test dataset.\n\n**各个特征的数据类型是什么样的?**\n\n在转换的目标时可以帮助我们.\n\n- 7 个特征是 integer 或 floats. 6 个在测试数据集中.\n- 5 个特征是 strings (object).\n\n\n```python\ntrain_df.info()\nprint('_'*40)\ntest_df.info()\n```\n\n    <class 'pandas.core.frame.DataFrame'>\n    RangeIndex: 891 entries, 0 to 890\n    Data columns (total 12 columns):\n    PassengerId    891 non-null int64\n    Survived       891 non-null int64\n    Pclass         891 non-null int64\n    Name           891 non-null object\n    Sex            891 non-null object\n    Age            714 non-null float64\n    SibSp          891 non-null int64\n    Parch          891 non-null int64\n    Ticket         891 non-null object\n    Fare           891 non-null float64\n    Cabin          204 non-null object\n    Embarked       889 non-null object\n    dtypes: float64(2), int64(5), object(5)\n    memory usage: 83.6+ KB\n    ________________________________________\n    <class 'pandas.core.frame.DataFrame'>\n    RangeIndex: 418 entries, 0 to 417\n    Data columns (total 11 columns):\n    PassengerId    418 non-null int64\n    Pclass         418 non-null int64\n    Name           418 non-null object\n    Sex            418 non-null object\n    Age            332 non-null float64\n    SibSp          418 non-null int64\n    Parch          418 non-null int64\n    Ticket         418 non-null object\n    Fare           417 non-null float64\n    Cabin          91 non-null object\n    Embarked       418 non-null object\n    dtypes: float64(2), int64(4), object(5)\n    memory usage: 36.0+ KB\n\n\n**样本中数值特征值的分布是什么?**\n\n这有助于我们确定, 除了其他早期的思考, 在实际问题领域的训练数据集是如何具有代表性的.\n\n- 总样本是 891 或者在泰坦尼克号（2,224）上实际旅客的 40%.\n- Survived（生存）是一个具有 0 或 1 值的分类特征.\n- 大约 38% 样本幸存了下来, 然而实际的幸存率是 32%.\n- 大多数旅客 (> 75%) 没有和父母或孩子一起旅行.\n- 近 30% 的旅客有兄弟姐妹 和/或 配偶.\n- 少数旅客 Fares（票价）差异显著 (<1%), 最高达 $512.\n- 很少有年长的旅客 (<1%) 在年龄范围 65-80.\n\n\n```python\ntrain_df.describe()\n# Review survived rate using `percentiles=[.61, .62]` knowing our problem description mentions 38% survival rate.\n# Review Parch distribution using `percentiles=[.75, .8]`\n# SibSp distribution `[.68, .69]`\n# Age and Fare `[.1, .2, .3, .4, .5, .6, .7, .8, .9, .99]`\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>714.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>446.000000</td>\n      <td>0.383838</td>\n      <td>2.308642</td>\n      <td>29.699118</td>\n      <td>0.523008</td>\n      <td>0.381594</td>\n      <td>32.204208</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>257.353842</td>\n      <td>0.486592</td>\n      <td>0.836071</td>\n      <td>14.526497</td>\n      <td>1.102743</td>\n      <td>0.806057</td>\n      <td>49.693429</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.420000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>223.500000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>20.125000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>7.910400</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>446.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>28.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>14.454200</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>668.500000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>38.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>31.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>891.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>80.000000</td>\n      <td>8.000000</td>\n      <td>6.000000</td>\n      <td>512.329200</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n**分类特征的分布是什么样的?**\n\n- Names（名称）特征在数据集中是唯一的 (count=unique=891)\n- Sex（性别）变量有两个可能的值, 男性为 65% (top=male, freq=577/count=891).\n- Cabin（房间号）值在样本中有重复. 或者几个旅客共享一个客舱.\n- Embarked（出发港）有 3 个可能的值. 大多数乘客使用 S 港口(top=S)\n- Ticket（船票号码）特征有很高 (22%) 的重复值 (unique=681).\n\n\n```python\ntrain_df.describe(include=['O'])\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Ticket</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>891</td>\n      <td>891</td>\n      <td>891</td>\n      <td>204</td>\n      <td>889</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>891</td>\n      <td>2</td>\n      <td>681</td>\n      <td>147</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>Mitchell, Mr. Henry Michael</td>\n      <td>male</td>\n      <td>1601</td>\n      <td>C23 C25 C27</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>1</td>\n      <td>577</td>\n      <td>7</td>\n      <td>4</td>\n      <td>644</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n### 基于数据分析的假设\n\n到目前为止, 基于数据分析, 我们得出以下假设.\n在采取适当的行动之前, 我们可能会进一步验证这些假设.\n\n**Correlating（相关）.**\n\n我们想知道每个特征与生存相关的程度.\n我们希望在项目早期做到这一点, 并将这些快速相关性与项目后期的模型相关性相匹配.\n\n**Completing（完整）.**\n\n1. 我们可能想要去补全丢失的 Age（年龄）特征，因为它肯定与生存相关.\n2. 我们也想要去补全丢失的 Embarked（出发港）特征, 因为它也可能与生存或者其它重要的特征相关联.\n\n**Correcting（校正）.**\n\n1. Ticket（船票号码）特征可能会从我们的分析中删除, 因为它包含了很高的重复比例 (22%), 并且票号和生存之间可能没有关联.\n2. Cabin（房间号）特征可能因为高度不完整而丢失, 或者在 训练和测试数据集中都包含许多 null 值.\n3. PassengerId（旅客ID）可能会从训练数据集中删除, 因为它对生存来说没有贡献.\n4. Name（名称）特征是比较不规范的, 可能不直接影响生产, 所以也许会删除.\n\n**Creating（创建）.**\n\n1. 我们可能希望创建一个名为 Family 的基于 Parch 和 SibSp 的新特征，以获取船上家庭成员的总数.\n2. 我们可能想要设计 Name 功能以将 Title 抽取为新特征.\n3. 我们可能要为 Age（年龄）段创建新的特征. 这将一个连续的数字特征转变为一个顺序的分类特征.\n4. 如果它有助于我们的分析, 我们也可能想要创建 Fare（票价）范围的特征。\n\n**Classifying（分类）.**\n\n根据前面提到的问题描述, 我们也可以增加我们的假设.\n\n1. Women (Sex=female) 更有可能幸存下来.\n2. Children (Age<?) 更有可能幸存下来. \n3. 上层阶级的旅客 (Pclass=1) 更有可能幸存下来.\n\n## 通过旋转特征进行分析\n\n为了确认我们的一些观察和假设, 我们可以快速分析我们的特征之间的相互关系.\n我们只能在这个阶段为没有任何空值的特征做到这一点.\n对于 Sex（性别），顺序的（Pclass）或离散的（SibSp，Parch）类型的特征, 这也是有意义的.\n\n- **Pclass** 我们观察到 Pclass = 1 和 Survived（分类＃3）之间的显着相关性（> 0.5）. 我们决定在我们的模型中包含这个特征.\n- **Sex** 在 Sex=female（性别=女性）的问题定义中确认了74％（分类＃1）的幸存率非常高的观察意见.\n- **SibSp and Parch** 这些特征对于某些值具有零相关性. 从这些单独的特征（创建＃1）派生一个特征或一组特征可能是最好的\n\n\n```python\ntrain_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Pclass</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0.629630</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>0.472826</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>0.242363</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\ntrain_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Sex</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>female</td>\n      <td>0.742038</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>male</td>\n      <td>0.188908</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\ntrain_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SibSp</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.535885</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0.464286</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.345395</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>0.250000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0.166667</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>8</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\ntrain_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Parch</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>0.600000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.550847</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.343658</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>0.200000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n## 通过可视化数据进行分析\n\n现在我们可以继续使用可视化分析数据来确认我们的一些假设.\n\n### 关联数值的特征\n\n让我们从理解数值的特征和解决方案目标（生存）之间的相关性开始.\n\n柱状图可用于分析连续的数字变量，如 Age（年龄），其中条带或范围将有助于识别有用的模式.\n直方图可以使用自动定义的 bins 或等分范围的 bins 来说明样本的分布.\n这有助于我们回答有关特定频段的问题（婴儿有更好的幸存率吗？）\n\n请注意，直方图可视化中的 x 轴表示样本或旅客的数量.\n\n**Observations（观察）.**\n\n- 婴儿（4 岁以下）存活率高.\n- 最老的乘客（年龄= 80）幸存下来.\n- 大量的 15-25 岁的孩子没有幸.\n- 大多数乘客在 15-35 年龄范围内.\n\n**Decisions（决策）.**\n\n这个简单的分析证实了我们的假设, 作为后续工作流程阶段的决策.\n\n- 在我们的模型训练中, 我们应该考虑年龄（我们假设分类＃2）.\n- 完成空值的年龄功能（完成＃1）.\n- 我们应该 band（组合）年龄组（创建＃3）.\n\n\n```python\ng = sns.FacetGrid(train_df, col='Survived')\ng.map(plt.hist, 'Age', bins=20)\n```\n\n\n\n\n    <seaborn.axisgrid.FacetGrid at 0xce233c8>\n\n\n\n\n![png](/img/competitions/getting-started/titanic/titanic_output_24_1.png)\n\n\n### 关联数字和顺序的特征\n\n我们可以结合多个特征使用一个图来确定其相关性.\n这可以通过具有数字值的数字和分类特征来完成。\n\n**Observations（观察）.**\n\n- Pclass=3 拥有最多的乘客，但大多数没有生存. 确认我们的分类假设 ＃2.\n- Pclass=2 和 Pclass = 3 的婴儿乘客大多存活. 进一步限定了我们的分类假设 ＃2.\n- Pclass=1 的大多数乘客幸存下来。 确认我们的分类假设 ＃3。\n- Pclass 在乘客的年龄分布方面有所不同.\n\n**Decisions（决策）.**\n\n- 考虑 Pclass 用于模型训练.\n\n\n```python\n# grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')\ngrid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)\ngrid.map(plt.hist, 'Age', alpha=.5, bins=20)\ngrid.add_legend();\n```\n\n\n![png](/img/competitions/getting-started/titanic/titanic_output_26_0.png)\n\n\n### 关联分类特征\n\n现在我们可以将分类特征与我们的解决方案目标关联起来.\n\n**Observations（观察）.**\n\n- Female（女性）旅客的幸存率比 male（男性）好得多. 确认分类（＃1）。\n- Embarked= C 的例外, 其中男性的成活率较高. 这可能是 Pclass 和 Embarked 之间的相关性, 反过来, Pclass 和 Survived 之间, 不一定是进入和生存直接相关。\n- 与 C 和 Q 港口的 Pclass = 2 相比, Pclass = 3 时男性的生存率更高. 完成（＃2）。\n- 出发港口的 Pclass=3 和男性乘客的生存率不同. 相关（＃1）。\n\n**Decisions（决策）.**\n\n- 增加 Sex 特征以用于模型训练.\n- 补全丢失值并添加 Embarked 特征以用于模型训练.\n\n\n```python\n# grid = sns.FacetGrid(train_df, col='Embarked')\ngrid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\ngrid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\ngrid.add_legend()\n```\n\n\n\n\n    <seaborn.axisgrid.FacetGrid at 0xd6625f8>\n\n\n\n\n![png](/img/competitions/getting-started/titanic/titanic_output_28_1.png)\n\n\n### 关联分类和数值的特征\n\n我们也可能想要关联分类特征（非数值的）和数值的特征.\n我们可以考虑将 Embarked（类别非数字）, Sex（类别非数字）, Fare（数字连续）与生存（分类数字）相关联.\n\n**Observations（观察）.**\n\n- Higher fare paying passengers had better survival. Confirms our assumption for creating (#4) fare ranges.\n- Port of embarkation correlates with survival rates. Confirms correlating (#1) and completing (#2).\n\n- 更高的票价付费旅客有更好的生存. 证实我们对创造（＃4）票价范围的假设.\n- 搭乘港口与生存率相关. 确认关联（＃1）和完成（＃2）.\n\n**Decisions（决策）.**\n\n- 考虑 banding（绑定）票价功能\n\n\n```python\n# grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})\ngrid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\ngrid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\ngrid.add_legend()\n```\n\n\n\n\n    <seaborn.axisgrid.FacetGrid at 0xd5cb198>\n\n\n\n\n![png](/img/competitions/getting-started/titanic/titanic_output_30_1.png)\n\n\n## 整理数据\n\n我们收集了关于我们的数据集和解决方案要求的一些假设和决策.\n到目前为止, 我们没有必要改变一个单个的特征或值来达到目标.\n让我们现在执行我们的决定和假设来 correcting(校正), creating（创建）和 completing（完整）目标.\n\n### 通过删除特征进行校正\n\n这是一个很好的开始执行目标. 通过丢弃特征, 我们正在处理更少的数据点. 加快我们的 notebook, 并简化分析.\n\n根据我们的假设和决策, 我们要放弃 Cabin（房间号）（更正＃2）和 Ticket（票号）（更正＃1）的特征.\n\n请注意, 如果适用, 我们将对训练和测试数据集进行操作, 以保持一致.\n\n\n```python\nprint(\"Before\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)\n\ntrain_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\ntest_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\ncombine = [train_df, test_df]\n\n\"After\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape\n```\n\n    Before (891, 12) (418, 11) (891, 12) (418, 11)\n\n\n\n\n\n    ('After', (891, 10), (418, 9), (891, 10), (418, 9))\n\n\n\n### 从现在的提取以创建性特征\n\n我们想要分析一下, Name 特征是否可以被设计来提取 titles（头衔）和 test（测试）头衔和 survival（生存）之间的相关性, 然后再删除Name 和 PassengerId 特征.\n\n在下面的代码中, 我们使用正则表达式提取 Title 特征.  正则表达式`(\\w+\\.)`匹配 Name 特征中以点号字符结尾的第一个单词.\n`expand = False` 标志返回一个 DataFrame.\n\n**Observations（观察）.**\n\n当我们绘制出 Title, Age 和 Survived 的图时, 我们可以发现以下观察.\n\n- 大多数 titles band 年龄组准确. 例如: 硕士学位的年龄平均为 5 年。\n- Title 中的生存年龄段略有不同.\n- 某些 Title 大多存活（夫人, 女士, 先生）或不（Don, Rev, Jonkheer）.\n\n**Decision（决策）.**\n\n- 我们决定保留模型训练的新 Title 特征.\n\n\n```python\nfor dataset in combine:\n    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n\npd.crosstab(train_df['Title'], train_df['Sex'])\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>Sex</th>\n      <th>female</th>\n      <th>male</th>\n    </tr>\n    <tr>\n      <th>Title</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Capt</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Col</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Countess</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Don</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Dr</th>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>Jonkheer</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Lady</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Major</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Master</th>\n      <td>0</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>Miss</th>\n      <td>182</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Mlle</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Mme</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Mr</th>\n      <td>0</td>\n      <td>517</td>\n    </tr>\n    <tr>\n      <th>Mrs</th>\n      <td>125</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Ms</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Rev</th>\n      <td>0</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>Sir</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n我们可以用更常见的头衔来替换很多头衔, 或者将它们分类为 `Rare`.\n\n\n```python\nfor dataset in combine:\n    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n \t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n\n    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n    \ntrain_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Title</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Master</td>\n      <td>0.575000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Miss</td>\n      <td>0.702703</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Mr</td>\n      <td>0.156673</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Mrs</td>\n      <td>0.793651</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Rare</td>\n      <td>0.347826</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n我们可以将 titles（头衔）转换为顺序的.\n\n\n```python\ntitle_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\nfor dataset in combine:\n    dataset['Title'] = dataset['Title'].map(title_mapping)\n    dataset['Title'] = dataset['Title'].fillna(0)\n\ntrain_df.head()\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n      <th>Embarked</th>\n      <th>Title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Braund, Mr. Owen Harris</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.2500</td>\n      <td>S</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>71.2833</td>\n      <td>C</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>Heikkinen, Miss. Laina</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7.9250</td>\n      <td>S</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>53.1000</td>\n      <td>S</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Allen, Mr. William Henry</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8.0500</td>\n      <td>S</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n现在我们可以放心地从训练和测试数据集中删除 Name 特征.\n我们也不需要训练数据集中的 PassengerId 特征.\n\n\n```python\ntrain_df = train_df.drop(['Name', 'PassengerId'], axis=1)\ntest_df = test_df.drop(['Name'], axis=1)\ncombine = [train_df, test_df]\ntrain_df.shape, test_df.shape\n```\n\n\n\n\n    ((891, 9), (418, 9))\n\n\n\n### 转换分类的特征\n\n现在我们可以将包含字符串的特征转换为数字值.\n这是大多数模型算法所要求的.\n这样做也将帮助我们实现特征完成目标.\n让我们开始将 Sex（性别）特征转换为名为 Gender（性别）的新特征, 其中 female=1, male=0.\n\n\n```python\nfor dataset in combine:\n    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n\ntrain_df.head()\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n      <th>Embarked</th>\n      <th>Title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.2500</td>\n      <td>S</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>71.2833</td>\n      <td>C</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7.9250</td>\n      <td>S</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>53.1000</td>\n      <td>S</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8.0500</td>\n      <td>S</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n### 完整化数值字连续特征\n\n现在我们应该开始估计和完成缺少或空值的特征.\n我们将首先为 Age（年龄）特征执行此操作.\n\n我们可以考虑三种方法来完整化一个数值连续的特征.\n\n1.简单的方法是在平均值和 [标准偏差](https://en.wikipedia.org/wiki/Standard_deviation) 之间生成随机数.\n\n2.更准确地猜测缺失值的方法是使用其他相关特征. 在我们的例子中, 我们注意到 Age（年龄）, Sex（性别）和 Pclass 之间的相关性. 猜测年龄值使用 [中位数](https://en.wikipedia.org/wiki/Median) Age 中的各种 Pclass 和 Gender 特征组合的值. 因此, Pclass=1 和 Gender=0，Pclass=1 和 Gender=1 的年龄中位数等等...\n\n3.结合方法 1 和 2. 因此. 不要根据中位数来猜测年龄值, 而应根据 Pclass 和 Sex 组合, 使用平均数和标准差之间的随机数.\n\n方法 1 和 3 将在我们的模型中引入随机噪声. 多次执行的结果可能会有所不同. 我们更喜欢方法 2.\n\n\n```python\n# grid = sns.FacetGrid(train_df, col='Pclass', hue='Gender')\ngrid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)\ngrid.map(plt.hist, 'Age', alpha=.5, bins=20)\ngrid.add_legend()\n```\n\n\n\n\n    <seaborn.axisgrid.FacetGrid at 0xece2e10>\n\n\n\n\n![png](/img/competitions/getting-started/titanic/titanic_output_44_1.png)\n\n\n让我们开始准备一个空数组, 以包含基于 Pclass x Gender 组合以猜测 Age 值.\n\n\n```python\nguess_ages = np.zeros((2,3))\nguess_ages\n```\n\n\n\n\n    array([[ 0.,  0.,  0.],\n           [ 0.,  0.,  0.]])\n\n\n\n现在我们迭代 Sex（0 或 1）和 Pclass（1, 2, 3）来计算 6 个组合的 Age 的猜测值.\n\n\n```python\nfor dataset in combine:\n    for i in range(0, 2):\n        for j in range(0, 3):\n            guess_df = dataset[(dataset['Sex'] == i) & \\\n                                  (dataset['Pclass'] == j+1)]['Age'].dropna()\n\n            # age_mean = guess_df.mean()\n            # age_std = guess_df.std()\n            # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\n\n            age_guess = guess_df.median()\n\n            # Convert random age float to nearest .5 age\n            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n            \n    for i in range(0, 2):\n        for j in range(0, 3):\n            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n                    'Age'] = guess_ages[i,j]\n\n    dataset['Age'] = dataset['Age'].astype(int)\n\ntrain_df.head()\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n      <th>Embarked</th>\n      <th>Title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>22</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.2500</td>\n      <td>S</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>38</td>\n      <td>1</td>\n      <td>0</td>\n      <td>71.2833</td>\n      <td>C</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>26</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7.9250</td>\n      <td>S</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>35</td>\n      <td>1</td>\n      <td>0</td>\n      <td>53.1000</td>\n      <td>S</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>35</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8.0500</td>\n      <td>S</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n让我们创建年龄段并确定与 Survived 的相关性.\n\n\n```python\ntrain_df['AgeBand'] = pd.cut(train_df['Age'], 5)\ntrain_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>AgeBand</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>(-0.08, 16.0]</td>\n      <td>0.550000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>(16.0, 32.0]</td>\n      <td>0.337374</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>(32.0, 48.0]</td>\n      <td>0.412037</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>(48.0, 64.0]</td>\n      <td>0.434783</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>(64.0, 80.0]</td>\n      <td>0.090909</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n让我们使用年龄段的顺序值来替换 Aage.\n\n\n```python\nfor dataset in combine:    \n    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\n    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n    dataset.loc[ dataset['Age'] > 64, 'Age']\ntrain_df.head()\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n      <th>Embarked</th>\n      <th>Title</th>\n      <th>AgeBand</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.2500</td>\n      <td>S</td>\n      <td>1</td>\n      <td>(16.0, 32.0]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>71.2833</td>\n      <td>C</td>\n      <td>3</td>\n      <td>(32.0, 48.0]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7.9250</td>\n      <td>S</td>\n      <td>2</td>\n      <td>(16.0, 32.0]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>53.1000</td>\n      <td>S</td>\n      <td>3</td>\n      <td>(32.0, 48.0]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8.0500</td>\n      <td>S</td>\n      <td>1</td>\n      <td>(32.0, 48.0]</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n我们不能删除 AgeBand 特征.\n\n\n```python\ntrain_df = train_df.drop(['AgeBand'], axis=1)\ncombine = [train_df, test_df]\ntrain_df.head()\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n      <th>Embarked</th>\n      <th>Title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.2500</td>\n      <td>S</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>71.2833</td>\n      <td>C</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7.9250</td>\n      <td>S</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>53.1000</td>\n      <td>S</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8.0500</td>\n      <td>S</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n### 结合现有特征创建新特征\n\n我们可以为 Parch 和 SibSp 结合的 FamilySize 创建一个新的特征.\n这将使我们能够从我们的数据集中删除 Parch 和 SibSp.\n\n\n```python\nfor dataset in combine:\n    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n\ntrain_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FamilySize</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0.724138</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>0.578431</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>0.552795</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>0.333333</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0.303538</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0.200000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>0.136364</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>11</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n我们可以创建另一个名为 IsAlone 特征.\n\n\n```python\nfor dataset in combine:\n    dataset['IsAlone'] = 0\n    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n\ntrain_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>IsAlone</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.505650</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.303538</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n让我们放弃 Parch, SibSp 和 FamilySize 特征, 转而使用 IsAlone 特征.\n\n\n```python\ntrain_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\ntest_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\ncombine = [train_df, test_df]\n\ntrain_df.head()\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>Fare</th>\n      <th>Embarked</th>\n      <th>Title</th>\n      <th>IsAlone</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>7.2500</td>\n      <td>S</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>71.2833</td>\n      <td>C</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>7.9250</td>\n      <td>S</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>53.1000</td>\n      <td>S</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8.0500</td>\n      <td>S</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n我们还可以创建一个结合 Pclass 和 Age 的人造特征.\n\n\n```python\nfor dataset in combine:\n    dataset['Age*Class'] = dataset.Age * dataset.Pclass\n\ntrain_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age*Class</th>\n      <th>Age</th>\n      <th>Pclass</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>3</td>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n### 完整化分类特征\n\nEmbarked（出发港）特征有 S, Q, C 三个基于出发港口的值.\n我们的训练集有两个丢失值.\n我们简单的使用最常发生的情况来填充它.\n\n\n```python\nfreq_port = train_df.Embarked.dropna().mode()[0]\nfreq_port\n```\n\n\n\n\n    'S'\n\n\n\n\n```python\nfor dataset in combine:\n    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n    \ntrain_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Embarked</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>C</td>\n      <td>0.553571</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Q</td>\n      <td>0.389610</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>S</td>\n      <td>0.339009</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n### 转换分类特征为数值的\n\n我们现在可以通过创建一个新的数字港特征来转换 EmbarkedFill 特征.\n\n\n```python\nfor dataset in combine:\n    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n\ntrain_df.head()\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>Fare</th>\n      <th>Embarked</th>\n      <th>Title</th>\n      <th>IsAlone</th>\n      <th>Age*Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>7.2500</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>71.2833</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>7.9250</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>53.1000</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8.0500</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n### 快速完整化兵转换数值的特征\n\n现在，我们可以在测试数据集使用模式下为单个缺失值完整化票价特征, 以获取此特征最常出现的值. 我们用一行代码来完成.\n\n请注意, 我们并没有创建中间用的新特征, 也没有对相关性进行任何进一步的分析以猜测丢失的特征, 因为我们只替换单个值. 完成目标达到了模型算法对非空值操作的期望要求.\n\n我们可能还想把票价四舍五入到小数点后两位, 因为它代表货币.\n\n\n```python\ntest_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\ntest_df.head()\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>Fare</th>\n      <th>Embarked</th>\n      <th>Title</th>\n      <th>IsAlone</th>\n      <th>Age*Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>892</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>7.8292</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>893</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2</td>\n      <td>7.0000</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>894</td>\n      <td>2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>9.6875</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>895</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>8.6625</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>896</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>12.2875</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n我们不创建 FareBand 特征.\n\n\n```python\ntrain_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\ntrain_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FareBand</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>(-0.001, 7.91]</td>\n      <td>0.197309</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>(7.91, 14.454]</td>\n      <td>0.303571</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>(14.454, 31.0]</td>\n      <td>0.454955</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>(31.0, 512.329]</td>\n      <td>0.581081</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n将 Fare 特征转换为基于 FareBand 的顺序值.\n\n\n```python\nfor dataset in combine:\n    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n    dataset['Fare'] = dataset['Fare'].astype(int)\n\ntrain_df = train_df.drop(['FareBand'], axis=1)\ncombine = [train_df, test_df]\n    \ntrain_df.head(10)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>Fare</th>\n      <th>Embarked</th>\n      <th>Title</th>\n      <th>IsAlone</th>\n      <th>Age*Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n并且测试数据集也一样.\n\n\n```python\ntest_df.head(10)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>Fare</th>\n      <th>Embarked</th>\n      <th>Title</th>\n      <th>IsAlone</th>\n      <th>Age*Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>892</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>893</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>894</td>\n      <td>2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>895</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>896</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>897</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>898</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>899</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>900</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>901</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n## 模型, 预测和解决方案\n\n现在我们准备训练模型并通过训练得到的模型预测结果。有60多种用于预测的模型可供选择。我们必须了解问题的类型和解决方案的要求，将模型数量缩小到少数几个。我们的问题是分类和回归问题，因为需要确定输出（生存与否）与其他变量或特征（性别，年龄，港口...）之间的关系。此外，我们的问题应该属于监督学习，因为我们用已知类别的数据集来训练我们的模型。有了监督学习、分类和回归这两个标准，我们可以将模型选择的范围缩小到几个。这些包括：\n- Logistic回归\n- KNN或K—近邻\n- 支持向量机\n- 朴素贝叶斯分类器\n- 决策树\n- 随机森林\n- 感知器\n- 人工神经网络\n- 相关向量机\n\n\n\n```python\nX_train = train_df.drop(\"Survived\", axis=1)\nY_train = train_df[\"Survived\"]\nX_test  = test_df.drop(\"PassengerId\", axis=1).copy()\nX_train.shape, Y_train.shape, X_test.shape\n```\n\n\n\n\n    ((891, 8), (891,), (418, 8))\n\n\n\nLogistic回归形式简单，易于建模，适合用于早期的工作流程。Logistics回归使用线性回归模型的预测结果去逼近真实标记的对数几率，形式为参数化的Logistics分布。参考维基百科[Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression).\n\n注意模型产生的“置信度评分”是基于训练集的。\n\n\n```python\n# Logistic Regression\n\nlogreg = LogisticRegression()\nlogreg.fit(X_train, Y_train)\nY_pred = logreg.predict(X_test)\nacc_log = round(logreg.score(X_train, Y_train) * 100, 2)\nacc_log\n```\n\n\n\n\n    80.359999999999999\n\n\n\n我们可以使用Logistic回归来验证我们之前对特征的创建所做的假设。这可以通过计算决策函数中的特征的系数来完成。\n\n系数为正说明该特征增加了结果的对数几率（因而增加了概率），系数为负说明该特征降低了结果的对数几率（从而降低了概率）\n\n- Sex特征有最高的正系数，意味着当Sex从男（0）变成女（1）时，Survived = 1的概率增加最多。\n- 相反地，随着Pclass特征的增加，Survived = 1的概率减少的最多。\n- Age * Class是一个很好的人造特征，因为它与Survived具有次高的负相关性。\n- Title特征有第二高的正相关系数。\n\n\n```python\ncoeff_df = pd.DataFrame(train_df.columns.delete(0))\ncoeff_df.columns = ['Feature']\ncoeff_df[\"Correlation\"] = pd.Series(logreg.coef_[0])\n\ncoeff_df.sort_values(by='Correlation', ascending=False)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Feature</th>\n      <th>Correlation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Sex</td>\n      <td>2.201527</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Title</td>\n      <td>0.398234</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Age</td>\n      <td>0.287164</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Embarked</td>\n      <td>0.261762</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>IsAlone</td>\n      <td>0.129140</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Fare</td>\n      <td>-0.085150</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Age*Class</td>\n      <td>-0.311199</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Pclass</td>\n      <td>-0.749006</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n接下来，我们使用支持向量机（SVM）模型。支持向量机是一个监督学习模型，它使用相关学习算法来分析数据，可以用于分类和回归问题。在二元分类的情况下，SVM算法建立一个模型，去找两类训练样本“正中间”的划分超平面，因为该划分超平面对训练样本局部扰动的“容忍性”最好。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Support_vector_machine).\n\n注意SVM模型生成的“置信度评分”高于Logistics回归模型。\n\n\n```python\n# Support Vector Machines\n\nsvc = SVC()\nsvc.fit(X_train, Y_train)\nY_pred = svc.predict(X_test)\nacc_svc = round(svc.score(X_train, Y_train) * 100, 2)\nacc_svc\n```\n\n\n\n\n    83.840000000000003\n\n\n\n在模式识别中，k-近邻算法（简称k-NN）是一种用于分类和回归的无参数方法。测试样本找出训练集中与其最靠近的k个训练样本，选择这k个样本中出现最多的类别标记作为预测结果（k是一个正整数，通常很小）。如果k = 1，则该对象的类别和最近邻样本的类别一致。 参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm).\n\nKNN的“置信度评分”比Logistics回归好，但比SVM差。\n\n\n```python\nknn = KNeighborsClassifier(n_neighbors = 3)\nknn.fit(X_train, Y_train)\nY_pred = knn.predict(X_test)\nacc_knn = round(knn.score(X_train, Y_train) * 100, 2)\nacc_knn\n```\n\n\n\n\n    84.739999999999995\n\n\n\n在机器学习中，朴素贝叶斯分类器是一个基于所有特征互相独立的贝叶斯理论的简单概率分类器。朴素贝叶斯分类器具有高度可扩展性，在学习过程中需要大量的线性特征作为参数。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier).\n\n该模型生成的“置信度评分”是目前模型中最低的。\n\n\n```python\n# Gaussian Naive Bayes\n\ngaussian = GaussianNB()\ngaussian.fit(X_train, Y_train)\nY_pred = gaussian.predict(X_test)\nacc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\nacc_gaussian\n```\n\n\n\n\n    72.280000000000001\n\n\n\n感知器是用于二元分类器的监督学习的算法（可以决定包含一个向量的输入是否属于某个类别）。它是一种线性分类器，即一种分类算法，通过一个线性预测函数将一组权重与特征向量组合来进行预测。该算法允许在线学习，因为它在一次迭代中只处理一个训练集中的元素。 参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Perceptron).\n\n\n```python\n# Perceptron\n\nperceptron = Perceptron()\nperceptron.fit(X_train, Y_train)\nY_pred = perceptron.predict(X_test)\nacc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\nacc_perceptron\n```\n\n    D:\\Anaconda\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.perceptron.Perceptron'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n      \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n\n\n\n\n\n    78.0\n\n\n\n\n```python\n# Linear SVC\n\nlinear_svc = LinearSVC()\nlinear_svc.fit(X_train, Y_train)\nY_pred = linear_svc.predict(X_test)\nacc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\nacc_linear_svc\n```\n\n\n\n\n    79.120000000000005\n\n\n\n\n```python\n# Stochastic Gradient Descent\n\nsgd = SGDClassifier()\nsgd.fit(X_train, Y_train)\nY_pred = sgd.predict(X_test)\nacc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\nacc_sgd\n```\n\n    D:\\Anaconda\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n      \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n\n\n\n\n\n    80.019999999999996\n\n\n\n该模型使用决策树作为预测模型，将特征（树的分支）映射到决策结果（树的叶结点）。目标变量是有限的一组值的树称为分类树; 在这些树结构中，叶结点对应于决策结果，其他每个结点对应于一个属性测试，每个结点包含的样本集合根据属性测试的结果被划分到子结点中。目标变量可以取连续值（通常是实数）的决策树称为回归树。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning).\n\n该模型的“置信度评分”是目前模型中最高的。\n\n\n```python\n# Decision Tree\n\ndecision_tree = DecisionTreeClassifier()\ndecision_tree.fit(X_train, Y_train)\nY_pred = decision_tree.predict(X_test)\nacc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\nacc_decision_tree\n```\n\n\n\n\n    86.760000000000005\n\n\n\n随机森林是最流行的模型之一。随机森林或随机决策树森林是一种用于分类，回归或其他任务的集成学习模型，它通过在训练时构造大量的决策树（n_estimators = 100），再使用某种策略将这些“个体学习器”结合起来。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Random_forest).\n\n该模型的“置信度评分”是目前模型中最高的。我们决定使用这个模型的输出（Y_pred）来作为竞赛结果。\n\n\n```python\n# Random Forest\n\nrandom_forest = RandomForestClassifier(n_estimators=100)\nrandom_forest.fit(X_train, Y_train)\nY_pred = random_forest.predict(X_test)\nrandom_forest.score(X_train, Y_train)\nacc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\nacc_random_forest\n```\n\n\n\n\n    86.760000000000005\n\n\n\n### 模型评估\n\n现在, 我们可以对所有模型进行评估, 为我们的问题选择最好的模型。\n虽然决策树和随机森林评分相同, 但我们选择使用随机森林，因为随机森林会校正决策树“过拟合”的缺点。\n\n\n```python\nmodels = pd.DataFrame({\n    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n              'Random Forest', 'Naive Bayes', 'Perceptron', \n              'Stochastic Gradient Decent', 'Linear SVC', \n              'Decision Tree'],\n    'Score': [acc_svc, acc_knn, acc_log, \n              acc_random_forest, acc_gaussian, acc_perceptron, \n              acc_sgd, acc_linear_svc, acc_decision_tree]})\nmodels.sort_values(by='Score', ascending=False)\n```\n\n\n\n\n<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Model</th>\n      <th>Score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td>Random Forest</td>\n      <td>86.76</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Decision Tree</td>\n      <td>86.76</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>KNN</td>\n      <td>84.74</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Support Vector Machines</td>\n      <td>83.84</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Logistic Regression</td>\n      <td>80.36</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Stochastic Gradient Decent</td>\n      <td>80.02</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Linear SVC</td>\n      <td>79.12</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Perceptron</td>\n      <td>78.00</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Naive Bayes</td>\n      <td>72.28</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nsubmission = pd.DataFrame({\n        \"PassengerId\": test_df[\"PassengerId\"],\n        \"Survived\": Y_pred\n    })\n# submission.to_csv('../output/submission.csv', index=False)\n```\n\n我们提交给竞赛网站 Kaggle 的比赛结果在 6,082 个参赛作品中获得 3883 名.\n当竞赛正在进行时，这个结果是具有指导意义的.\n这个结果只占提交数据集的一部分.\n对我们的第一次尝试是不错的.\n欢迎任何提高我们的分数的建议.\n\n## 参考文献\n\n该手册是基于完成解决《泰坦尼克号》竞赛和其它来源的伟大工作而创建的.\n\n- [泰坦尼克号之旅](https://www.kaggle.com/omarelgabry/titanic/a-journey-through-titanic)\n- [ Pandas 入门指南: Kaggle 的泰坦尼克号竞赛](https://www.kaggle.com/c/titanic/details/getting-started-with-random-forests)\n- [泰坦尼克号的最佳处理分类器](https://www.kaggle.com/sinakhorami/titanic/titanic-best-working-classifier)\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/titanic/titanic_yy.md",
    "content": "# Titanic 第一次试水 | 瑶瑶亲卫队\n\n* fork from https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python\n* 开源组织: [ApacheCN ~ apachecn.org](http://www.apachecn.org) \n* team：瑶瑶亲卫队\n* 鸣谢：咸鱼大佬，帮忙调试错误，并上传\n\n我们主要根据这个 [kernel](https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python) 写的，当然生成的 submission ，已经提交到 kaggle 上评分出来了。\n\n## 目录\n\n* 1、加载我们要用到的库\n* 2、特征工程\n* 3、可视化\n* 4、整合模型\n* 5、生成基本的模型\n* 6、训练模型\n* 7、生成 submission 文件\n\n\n## 1、加载我们要用到的库\n\n```python\n# 加载我们的库\nimport pandas as pd\nimport numpy as np\n# 使用 re 写正则匹配\nimport re\n# 使用 sklearn 的模型来进行预测\nimport sklearn\n# 使用 seaborn 进行可视化\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport plotly.offline as py\npy.init_notebook_mode(connected=True)\nimport plotly.graph_objs as go\nimport plotly.tools as tls\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# 我们使用 5 种模型来预测\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.cross_validation import KFold\n```\n\n## 2、特征工程\n\n### 2.1、初步查看\n\n```python\n# 加载 train 和 test 数据集\ntrain = pd.read_csv('../input/train.csv')\ntest = pd.read_csv('../input/test.csv')\n\n# 存储 Passenger ID 方便后续操作\nPassengerId = test['PassengerId']\n\ntrain.head(3)\n```\n\n是下面这样(NaN 不是咱们想要的结果)：\n\n![train前3行](/img/competitions/getting-started/titanic/titanic_yy_1.png)\n\n\n### 2.2、加入一些特征，删除一些特征，映射一些特征\n\n```python\n# 整体的全部数据\nfull_data = [train, test]\n\n# 一些从原始 features 中衍生出的 features ，我认为可以算是一个 feature\n# Name_length 特征\ntrain['Name_length'] = train['Name'].apply(len)\ntest['Name_length'] = test['Name'].apply(len)\n# 在 Titanic 上是否具有一个 cabin\ntrain['Has_Cabin'] = train[\"Cabin\"].apply(lambda x: 0 if type(x) == float else 1)\ntest['Has_Cabin'] = test[\"Cabin\"].apply(lambda x: 0 if type(x) == float else 1)\n\n# 创建一个新的 feature FamilySize 作为 SibSp 和 Parch 的混合 features\nfor dataset in full_data:\n    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n# 从 FamilySize 特征中 创建一个新的 feature IsAlone \nfor dataset in full_data:\n    dataset['IsAlone'] = 0\n    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n# 删除 Embarked 列中的所有的 NULLS值，使用 S 来填充（算是超级暴力的吧）\nfor dataset in full_data:\n    dataset['Embarked'] = dataset['Embarked'].fillna('S')\n# 删除 Fare 中所有的 NULLS 值，并生成一个新的 feature 列 CategoricalFare\nfor dataset in full_data:\n    dataset['Fare'] = dataset['Fare'].fillna(train['Fare'].median())\ntrain['CategoricalFare'] = pd.qcut(train['Fare'], 4)\n# 创建一个新特征 CategoricalAge\nfor dataset in full_data:\n    age_avg = dataset['Age'].mean()\n    age_std = dataset['Age'].std()\n    age_null_count = dataset['Age'].isnull().sum()\n    age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)\n    dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list\n    dataset['Age'] = dataset['Age'].astype(int)\ntrain['CategoricalAge'] = pd.cut(train['Age'], 5)\n\n# 定义函数从 passenger 名字中获取 titles\ndef get_title(name):\n    title_search = re.search(' ([A-Za-z]+)\\.', name)\n    # 如果 title 存在，返回\n    if title_search:\n        return title_search.group(1)\n    return \"\"\n# 创建一个新的特征 Title, 包含乘客的名字的 titles\nfor dataset in full_data:\n    dataset['Title'] = dataset['Name'].apply(get_title)\n# 将所有的非常见的 title 分类到一个 “Rare” 特征\nfor dataset in full_data:\n    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n\n    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n\n# 将数据集中的数据映射成离散型数据\nfor dataset in full_data:\n    # 映射 Sex\n    dataset['Sex'] = dataset['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n    \n    # 映射 titles\n    title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n    dataset['Title'] = dataset['Title'].map(title_mapping)\n    dataset['Title'] = dataset['Title'].fillna(0)\n    \n    # 映射 Embarked\n    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n    \n    # 映射 Fare\n    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] \t\t\t\t\t\t        = 0\n    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n    dataset.loc[ dataset['Fare'] > 31, 'Fare'] \t\t\t\t\t\t\t        = 3\n    dataset['Fare'] = dataset['Fare'].astype(int)\n    \n    # 映射 Age\n    dataset.loc[ dataset['Age'] <= 16, 'Age'] \t\t\t\t\t       = 0\n    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n    dataset.loc[ dataset['Age'] > 64, 'Age'] = 4\n```\n\n选择一些特征：\n\n```python\n# Feature selection\ndrop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp']\ntrain = train.drop(drop_elements, axis = 1)\ntrain = train.drop(['CategoricalAge', 'CategoricalFare'], axis = 1)\ntest  = test.drop(drop_elements, axis = 1)\n```\n\n### 2.3、将新的数据特征存储起来（没有必要，我这儿闲的蛋疼）\n\n我将这些新的数据特征存储起来了，其实完全没有必要。\n\n```python\n# 生成一个临时文件，专门存储我们已经转化/映射完成之后的 train 和 test 文件，我写的很烂，，，\ndef saveTmpTrainFile(tmpFile,csvName):\n    with open(csvName, 'w', newline='') as myFile:\n        myWriter=csv.writer(myFile)\n        myWriter.writerow([\"Survived\",\"Pclass\",\"Sex\",\"Age\",\"Parch\",\"Fare\",\"Embarked\",\"Name_length\",\"Has_cabin\",\"FamilySize\",\"IsAlone\",\"Title\"])\n        for lines in tmpFile.index:\n            tmp=[]\n            tmp.append(tmpFile.loc[lines].values[1])\n            tmp.append(tmpFile.loc[lines].values[2])\n            tmp.append(tmpFile.loc[lines].values[4])\n            tmp.append(tmpFile.loc[lines].values[5])\n            tmp.append(tmpFile.loc[lines].values[7])\n            tmp.append(tmpFile.loc[lines].values[9])\n            tmp.append(tmpFile.loc[lines].values[11])\n            tmp.append(tmpFile.loc[lines].values[12])\n            tmp.append(tmpFile.loc[lines].values[13])\n            tmp.append(tmpFile.loc[lines].values[14])\n            tmp.append(tmpFile.loc[lines].values[15])\n            tmp.append(tmpFile.loc[lines].values[-1])\n            myWriter.writerow(tmp)\n            \nsaveTmpTrainFile(train,'D:/titanic/titanic_dataset/train_later.csv')\n\n# 生成一个临时文件，专门存储我们已经转化/映射完成之后的 train 和 test 文件\ndef saveTmpTestFile(tmpFile,csvName):\n    with open(csvName, 'w', newline='') as myFile:\n        myWriter=csv.writer(myFile)\n        myWriter.writerow([\"Pclass\",\"Sex\",\"Age\",\"Parch\",\"Fare\",\"Embarked\",\"Name_length\",\"Has_cabin\",\"FamilySize\",\"IsAlone\",\"Title\"])\n        for lines in tmpFile.index:\n            tmp=[]\n            tmp.append(tmpFile.loc[lines].values[1])\n            tmp.append(tmpFile.loc[lines].values[3])\n            tmp.append(tmpFile.loc[lines].values[4])\n            tmp.append(tmpFile.loc[lines].values[6])\n            tmp.append(tmpFile.loc[lines].values[8])\n            tmp.append(tmpFile.loc[lines].values[10])\n            tmp.append(tmpFile.loc[lines].values[11])\n            tmp.append(tmpFile.loc[lines].values[12])\n            tmp.append(tmpFile.loc[lines].values[13])\n            tmp.append(tmpFile.loc[lines].values[14])\n            tmp.append(tmpFile.loc[lines].values[15])\n            myWriter.writerow(tmp)\n            \n# 存储 test 文件\nsaveTmpFile(test,'D:/titanic/titanic_dataset/test_later.csv')\n```\n\n## 3、可视化\n\n我们处理完成之后的 train 数据集为：\n\n![train前3行](/img/competitions/getting-started/titanic/titanic_yy_2.png)\n\n让我们生成一些特征的关联图，以查看一个特征与下一个特征之间的相关性。 为此，我们将利用 Seaborn 绘图软件包，使我们能够非常方便地绘制热图，如下所示：\n\n```python\n<matplotlib.axes._subplots.AxesSubplot at 0x7f714b716048>\n```\n\n![train前3行](/img/competitions/getting-started/titanic/titanic_yy_3.png)\n\n皮尔逊相关图可以告诉我们的一件事是，没有太多的特征与彼此强相关。 从将这些特征提供给您的学习模型的角度来看，这是很好的，因为这意味着我们的训练集中没有太多冗余或多余的数据，我们很高兴每个特征都带有一些独特的信息。 这里有两个最相关的功能是 FamilySize 和 Parch（家长和儿童）。但是这儿没有删除，依然留着。\n\n\n## 4、整合模型\n\n我们这儿使用一个 SklearnHelper 类，来方便我们的面向对象编程，它允许扩展所有 Sklearn 分类器共有的内置方法（如 train, predict 和 fit）。因此，如果我们想要调用五个不同的分类器，就可以省去五次冗余。\n\n```python\n# 加载我们生成的 train 和 test 文件\n# # -----------注意一下哈，这个地方，需要调整一下生成的文件-------------------------------------------------------------\ntrain_later = pd.read_csv('D:/titanic/titanic_dataset/train_later.csv')\ntest_later = pd.read_csv('D:/titanic/titanic_dataset/test_later.csv')\n\n# train.head(3)\n# test.head(3)\n\n# # 使用 seaborn 将 特征的 Person 系数画出来\n# colormap = plt.cm.RdBu\n# plt.figure(figsize=(14,12))\n# plt.title('Pearson Correlation of Features', y=1.05, size=15)\n# sns.heatmap(train.astype(float).corr(),linewidths=0.1,vmax=1.0, \n#             square=True, cmap=colormap, linecolor='white', annot=True)\n\n# 一些比较有用参数，后边会派上用场\nntrain = train_later.shape[0]\nntest = test_later.shape[0]\nSEED = 0 # 重现性\nNFOLDS = 5 # 设置交叉验证的折数，以便后面的 K 折交叉验证\nkf = KFold(ntrain, n_folds= NFOLDS, random_state=SEED)\n\n# sklearn 的分类器\nclass SklearnHelper(object):\n    def __init__(self, clf, seed=0, params=None):\n        params['random_state'] = seed\n        self.clf = clf(**params)\n\n    def train(self, x_train, y_train):\n        self.clf.fit(x_train, y_train)\n\n    def predict(self, x):\n        return self.clf.predict(x)\n    \n    def fit(self,x,y):\n        return self.clf.fit(x,y)\n    \n    def feature_importances(self,x,y):\n        print(self.clf.fit(x,y).feature_importances_)\n\n# 获取最后的 预测结果        \ndef get_oof(clf, x_train, y_train, x_test):\n    oof_train = np.zeros((ntrain,))\n    oof_test = np.zeros((ntest,))\n    oof_test_skf = np.empty((NFOLDS, ntest))\n\n    for i, (train_index, test_index) in enumerate(kf):\n        x_tr = x_train[train_index]\n        y_tr = y_train[train_index]\n        x_te = x_train[test_index]\n\n        clf.train(x_tr, y_tr)\n\n        oof_train[test_index] = clf.predict(x_te)\n        oof_test_skf[i, :] = clf.predict(x_test)\n\n    oof_test[:] = oof_test_skf.mean(axis=0)\n    return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)\n```\n\n## 5、生成基本模型\n\n以下是 sklearn 中的 5 个模型，都可以很方便的调用\n\n* Random Forest classifier --- Score 0.78947\n* Extra Trees classifier --- Score 0.79425\n* AdaBoost classifer --- Score  0.74162\n* Gradient Boosting classifer --- Score 0.77033\n* Support Vector Machine  --- Score 0.77990\n\n参数：\n\n* n_jobs：用于训练过程的 core 的数量。如果设置为-1，则使用所有内核。\n* n_estimators：学习模型中分类树的数量（默认设置为10）\n* max_depth：树的最大深度，或者应该展开多少节点。要小心，如果设置得太高，就会有过度配合的风险，因为会使树太深\n* verbose：控制在学习过程中是否要输出任何文本。值为0会抑制所有的文本，而值3会在每次迭代时输出树学习过程。\n\n详细的参数介绍，请参考 [sklearn 官方文档](http://sklearn.apachecn.org/cn/0.19.0/)。\n\n```python\n# 将参数加载到调用的分类器中，进行调试\n# Random Forest 的参数\nrf_params = {\n    'n_jobs': -1,\n    'n_estimators': 500,\n     'warm_start': True, \n     #'max_features': 0.2,\n    'max_depth': 6,\n    'min_samples_leaf': 2,\n    'max_features' : 'sqrt',\n    'verbose': 0\n}\n\n# Extra Trees 的参数\net_params = {\n    'n_jobs': -1,\n    'n_estimators':500,\n    #'max_features': 0.5,\n    'max_depth': 8,\n    'min_samples_leaf': 2,\n    'verbose': 0\n}\n\n# AdaBoost 的参数\nada_params = {\n    'n_estimators': 500,\n    'learning_rate' : 0.75\n}\n\n# Gradient Boosting 的参数\ngb_params = {\n    'n_estimators': 500,\n     #'max_features': 0.2,\n    'max_depth': 5,\n    'min_samples_leaf': 2,\n    'verbose': 0\n}\n\n# Support Vector Classifier 的参数\nsvc_params = {\n    'kernel' : 'linear',\n    'C' : 0.025\n    }\n```\n\n## 6、训练模型\n\n```python\n# 创建代表模型的 5 个对象\nrf = SklearnHelper(clf=RandomForestClassifier, seed=SEED, params=rf_params)\net = SklearnHelper(clf=ExtraTreesClassifier, seed=SEED, params=et_params)\nada = SklearnHelper(clf=AdaBoostClassifier, seed=SEED, params=ada_params)\ngb = SklearnHelper(clf=GradientBoostingClassifier, seed=SEED, params=gb_params)\nsvc = SklearnHelper(clf=SVC, seed=SEED, params=svc_params)\n\n# 创建 Numpy arrays 来代表 train, test 和 target\ny_train = train['Survived'].ravel()\ntrain = train.drop(['Survived'], axis=1)\nx_train = train.values # 创建 train 的 array\nx_test = test.values # 创建 test 的 array\n\n# 创建代表模型的 5 个对象\nrf = SklearnHelper(clf=RandomForestClassifier, seed=SEED, params=rf_params)\net = SklearnHelper(clf=ExtraTreesClassifier, seed=SEED, params=et_params)\nada = SklearnHelper(clf=AdaBoostClassifier, seed=SEED, params=ada_params)\ngb = SklearnHelper(clf=GradientBoostingClassifier, seed=SEED, params=gb_params)\nsvc = SklearnHelper(clf=SVC, seed=SEED, params=svc_params)\n\n# 创建 Numpy arrays 来代表 train, test 和 target\ny_train = train['Survived'].ravel()\ntrain = train.drop(['Survived'], axis=1)\nx_train = train.values # 创建 train 的 array\nx_test = test.values # 创建 test 的 array\n\n# 创建您的 OOF 的 train 和 test 预测\net_oof_train, et_oof_test = get_oof(et, x_train, y_train, x_test) # Extra Trees\nrf_oof_train, rf_oof_test = get_oof(rf,x_train, y_train, x_test) # Random Forest\nada_oof_train, ada_oof_test = get_oof(ada, x_train, y_train, x_test) # AdaBoost \ngb_oof_train, gb_oof_test = get_oof(gb,x_train, y_train, x_test) # Gradient Boost\nsvc_oof_train, svc_oof_test = get_oof(svc,x_train, y_train, x_test) # Support Vector Classifier\n\nprint(\"Training is complete\")\n```\n\n## 7、生成 submission 结果\n\n生成文件的位置都需要自定义以下：\n\n```python\n# 创建 submission 文件，接下来就是提交到 kaggle 页面看一下得分和排名啦\ndef saveResult(result,csvName):\n    with open(csvName,'w',newline='') as myFile:    \n        myWriter=csv.writer(myFile)\n        myWriter.writerow([\"PassengerId\",\"Survived\"])\n        index=891\n        for i in result:\n            tmp=[]\n            index=index+1\n            tmp.append(index)\n            tmp.append(int(i))\n            myWriter.writerow(tmp)\n            \n\nsaveResult(et_oof_test,'D:/titanic/titanic_dataset/result/et.csv')\nsaveResult(rf_oof_test,'D:/titanic/titanic_dataset/result/rf.csv')\nsaveResult(ada_oof_test,'D:/titanic/titanic_dataset/result/ada.csv')\nsaveResult(gb_oof_test,'D:/titanic/titanic_dataset/result/gb.csv')\nsaveResult(svc_oof_test,'D:/titanic/titanic_dataset/result/svc.csv')\n```\n\n将生成的文件在 [这里](https://www.kaggle.com/c/titanic/submit) 提交，看一下评分。"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/NLP电影预测.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# **word2vec nlp tutorial**\\n\",\n    \"\\n\",\n    \"> 比赛说明\\n\",\n    \"\\n\",\n    \"标记的数据集由50,000条IMDB电影评论组成，专门用于情感分析。评论的情感是二元的，这意味着IMDB评分<5导致情绪分数为0，并且评分≥7的情绪评分为1.没有单独的电影具有超过30个评论。25,000个带有复审标签的训练集不包含与25,000个复习测试集相同的电影。此外，还有另外50,000个IMDB评论没有提供任何评级标签。\\n\",\n    \"\\n\",\n    \"> 文件说明\\n\",\n    \"\\n\",\n    \"| 文件 | 说明 |\\n\",\n    \"| :--- | :--- |\\n\",\n    \"| labeledTrainData | 标记的训练集。该文件是制表符分隔的，并且有一个标题行，后面跟着25,000行，其中包含每个审阅的ID，情绪和文本。 |\\n\",\n    \"| testData | 测试集。制表符分隔的文件有一个标题行，后面跟着25,000行，其中包含每个评论的标识和文本。你的任务是预测每个人的情绪。 |\\n\",\n    \"| unlabeledTrainData | 一个没有标签的额外训练集。制表符分隔的文件有一个标题行，后跟50,000行，每行包含一个标识和文本。  |\\n\",\n    \"| sampleSubmission | 以正确格式的逗号分隔的示例提交文件。 |\\n\",\n    \"\\n\",\n    \"> 数据字段\\n\",\n    \"\\n\",\n    \"| 字段 | 说明 |\\n\",\n    \"| :--- | :--- |\\n\",\n    \"| id | 每个评论的唯一ID |\\n\",\n    \"| sentiment | 审查的情绪; 1为正面评论，0为负面评论 |\\n\",\n    \"| review | 审查的文本 |\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 比赛操作流程\\n\",\n    \"\\n\",\n    \"分类问题：预测的是好与坏的问题\\n\",\n    \"常用算法： K紧邻(knn)、逻辑回归(LogisticRegression)、随机森林(RandomForest)、支持向量机(SVM)、xgboost、GBDT\\n\",\n    \"\\n\",\n    \"> 步骤:\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"一. 数据分析\\n\",\n    \"1. 下载并加载数据\\n\",\n    \"2. 总体预览:了解每列数据的含义,数据的格式等\\n\",\n    \"3. 数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备\\n\",\n    \"\\n\",\n    \"二. 特征工程\\n\",\n    \"1.根据业务,常识,以及第二步的数据分析构造特征工程.\\n\",\n    \"2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)\\n\",\n    \"\\n\",\n    \"三. 模型选择\\n\",\n    \"1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.\\n\",\n    \"2.比较各个模型的分数,然后取效果较好的模型作为基础模型.\\n\",\n    \"\\n\",\n    \"四. 模型融合\\n\",\n    \"\\n\",\n    \"五. 修改特征和模型参数\\n\",\n    \"1.可以通过添加或者修改特征,提高模型的上限.\\n\",\n    \"2.通过修改模型的参数,是模型逼近上限\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"* * * \\n\",\n    \"\\n\",\n    \"* 比赛地址: https://www.kaggle.com/c/word2vec-nlp-tutorial\\n\",\n    \"* 参考地址: https://www.cnblogs.com/zhao441354231/p/6056914.html\\n\",\n    \"* 参考地址: https://blog.csdn.net/lijingpengchina/article/details/52250765\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 一.数据分析\\n\",\n    \"\\n\",\n    \"### 数据下载和加载\\n\",\n    \"\\n\",\n    \"* 数据集下载地址: https://www.kaggle.com/c/word2vec-nlp-tutorial/data\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 导入相关数据包\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from bs4 import *\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 读取数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"data_dir = \\\"/opt/data/kaggle/getting-started/word2vec-nlp-tutorial\\\"\\n\",\n    \"# 载入数据集       \\n\",\n    \"train = pd.read_csv(os.path.join(data_dir, 'labeledTrainData.tsv'), header=0, delimiter=\\\"\\\\t\\\", quoting=3)\\n\",\n    \"pre = pd.read_csv(os.path.join(data_dir, 'testData.tsv'), header=0, delimiter=\\\"\\\\t\\\", quoting=3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(25000, 3) \\t (25000, 2) \\t\\n\",\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"RangeIndex: 25000 entries, 0 to 24999\\n\",\n      \"Data columns (total 3 columns):\\n\",\n      \"id           25000 non-null object\\n\",\n      \"sentiment    25000 non-null int64\\n\",\n      \"review       25000 non-null object\\n\",\n      \"dtypes: int64(1), object(2)\\n\",\n      \"memory usage: 586.0+ KB\\n\",\n      \"None \\n\",\n      \"\\n\",\n      \"\\n\",\n      \" ['id' 'sentiment' 'review']\\n\",\n      \"\\n\",\n      \"          id  sentiment                                             review\\n\",\n      \"0  \\\"5814_8\\\"          1  \\\"With all this stuff going down at the moment ...\\n\",\n      \"1  \\\"2381_9\\\"          1  \\\"\\\\\\\"The Classic War of the Worlds\\\\\\\" by Timothy ...\\n\",\n      \"2  \\\"7759_3\\\"          0  \\\"The film starts with a manager (Nicholas Bell...\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(train.shape, '\\\\t', pre.shape, '\\\\t')\\n\",\n    \"print(train.info(), '\\\\n')\\n\",\n    \"print('\\\\n', train.columns.values)\\n\",\n    \"print('\\\\n', train.head(3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 数据预处理\\n\",\n    \"\\n\",\n    \"* 1.去掉html标签\\n\",\n    \"* 2.移除标点\\n\",\n    \"* 3.切分成词/token\\n\",\n    \"* 4.去掉停用词\\n\",\n    \"* 5.重组为新的句子\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# def review_to_wordlist(review):\\n\",\n    \"#     '''\\n\",\n    \"#     把IMDB的评论转成词序列\\n\",\n    \"#     参考：http://blog.csdn.net/longxinchen_ml/article/details/50629613\\n\",\n    \"#     '''\\n\",\n    \"#     # 去掉HTML标签，拿到内容\\n\",\n    \"#     review_text = BeautifulSoup(review, \\\"html.parser\\\").get_text()\\n\",\n    \"#     # 用正则表达式取出符合规范的部分\\n\",\n    \"#     review_text = re.sub(\\\"[^a-zA-Z]\\\", \\\" \\\", review_text)\\n\",\n    \"#     # 小写化所有的词，并转成词list\\n\",\n    \"#     words = review_text.lower().split()\\n\",\n    \"#     # 返回words\\n\",\n    \"#     return words\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# 预处理数据\\n\",\n    \"label = train['sentiment']\\n\",\n    \"train_data = []\\n\",\n    \"pre_data = []\\n\",\n    \"for i in range(len(train['review'])):\\n\",\n    \"    train_data.append(BeautifulSoup(train['review'][i], \\\"html.parser\\\").get_text())\\n\",\n    \"test_data = []\\n\",\n    \"for i in range(len(pre['review'])):\\n\",\n    \"    pre_data.append(BeautifulSoup(pre['review'][i], \\\"html.parser\\\").get_text())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\\"With all this stuff going down at the moment with MJ i've started listening to his music, watching the odd documentary here and there, watched The Wiz and watched Moonwalker again. Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent. Moonwalker is part biography, part feature film which i remember going to see at the cinema when it was originally released. Some of it has subtle messages about MJ's feeling towards the press and also the obvious message of drugs are bad m'kay.Visually impressive but of course this is all about Michael Jackson so unless you remotely like MJ in anyway then you are going to hate this and find it boring. Some may call MJ an egotist for consenting to the making of this movie BUT MJ and most of his fans would say that he made it for the fans which if true is really nice of him.The actual feature film bit when it finally starts is only on for 20 minutes or so excluding the Smooth Criminal sequence and Joe Pesci is convincing as a psychopathic all powerful drug lord. Why he wants MJ dead so bad is beyond me. Because MJ overheard his plans? Nah, Joe Pesci's character ranted that he wanted people to know it is he who is supplying drugs etc so i dunno, maybe he just hates MJ's music.Lots of cool things in this like MJ turning into a car and a robot and the whole Speed Demon sequence. Also, the director must have had the patience of a saint when it came to filming the kiddy Bad sequence as usually directors hate working with one kid let alone a whole bunch of them performing a complex dance scene.Bottom line, this movie is for people who like MJ on one level or another (which i think is most people). If not, then stay away. It does try and give off a wholesome message and ironically MJ's bestest buddy in this movie is a girl! Michael Jackson is truly one of the most talented people ever to grace this planet but is he guilty? Well, with all the attention i've gave this subject....hmmm well i don't know because people can be different behind closed doors, i know this for a fact. He is either an extremely nice but stupid guy or one of the most sickest liars. I hope he is not the latter.\\\" \\n\",\n      \"\\n\",\n      \"\\\"Naturally in a film who's main themes are of mortality, nostalgia, and loss of innocence it is perhaps not surprising that it is rated more highly by older viewers than younger ones. However there is a craftsmanship and completeness to the film which anyone can enjoy. The pace is steady and constant, the characters full and engaging, the relationships and interactions natural showing that you do not need floods of tears to show emotion, screams to show fear, shouting to show dispute or violence to show anger. Naturally Joyce's short story lends the film a ready made structure as perfect as a polished diamond, but the small changes Huston makes such as the inclusion of the poem fit in neatly. It is truly a masterpiece of tact, subtlety and overwhelming beauty.\\\"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 预览数据\\n\",\n    \"print(train_data[0], '\\\\n')\\n\",\n    \"print(pre_data[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 特征处理\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 合并训练和测试集以便进行TFIDF向量化操作\\n\",\n    \"data_all = train_data + pre_data\\n\",\n    \"len_train = len(train_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"直接丢给计算机这些词文本，计算机是无法计算的，因此我们需要把文本转换为向量，有几种常见的文本向量处理方法，比如： \\n\",\n    \"\\n\",\n    \"1. Bow-of-Words计数 \\n\",\n    \"2. TF-IDF向量 \\n\",\n    \"3. Word2vec向量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['have', 'yourselves', 'itself', \\\"haven't\\\", 'y', 'shan', 'because', 'didn', 'it', \\\"she's\\\", 'nor', 'once', 'hadn', 'an', 'will', 'in', 'than', 'just', \\\"doesn't\\\", 'down', \\\"mightn't\\\", 've', 'shouldn', 'before', 'when', 'and', 'won', 'which', \\\"wouldn't\\\", 'other', 'are', 'doesn', 'here', 'him', 'why', \\\"mustn't\\\", 'theirs', 'ours', 'himself', 'now', 'at', 'but', 'its', 'were', 'whom', 'how', 'again', 'under', 'myself', 'me', 'your', 'then', 'he', 'the', 'who', 'herself', 'off', 'aren', 'each', 'same', 'all', \\\"that'll\\\", 'so', 'having', 'that', 'couldn', 'she', 'wasn', 'own', \\\"shouldn't\\\", 'by', 'there', 'this', 'we', 'if', 'no', 'doing', 'don', 'ain', \\\"you've\\\", 'had', 't', 'into', 'too', 'hasn', 'they', 'few', 'their', 'being', 'mightn', \\\"you'd\\\", 'a', 'her', \\\"couldn't\\\", 'did', \\\"you'll\\\", 'd', 'can', 'been', 'm', 'yours', 'very', 'wouldn', 'i', 'his', 'during', 'through', 'you', 'against', 'be', 'themselves', 'not', 'out', \\\"don't\\\", 'is', \\\"it's\\\", 'was', 'does', 'ma', 'needn', 'these', 'some', 'on', \\\"isn't\\\", 'for', 'further', \\\"hadn't\\\", 'isn', 'below', 'more', \\\"didn't\\\", 'has', 'up', 'with', 'about', \\\"weren't\\\", 'am', 'those', 'where', 'what', 'any', 's', \\\"you're\\\", 'do', 'or', 'over', 'weren', 'my', 'until', 'as', 'most', 'only', \\\"should've\\\", 'ourselves', \\\"needn't\\\", 'haven', 'above', 'such', 'hers', \\\"shan't\\\", 'after', 'while', \\\"wasn't\\\", 'them', 'between', 'our', 'from', 'yourself', \\\"aren't\\\", 'should', 'mustn', \\\"hasn't\\\", \\\"won't\\\", 'to', 're', 'of', 'both', 'o', 'll']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from nltk.corpus import stopwords\\n\",\n    \"#英文停止词，set()集合函数消除重复项\\n\",\n    \"list_stopWords = list(set(stopwords.words('english')))\\n\",\n    \"print(list_stopWords)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from gensim import corpora\\n\",\n    \"\\n\",\n    \"# bow 模型    \\n\",\n    \"import re\\n\",\n    \"texts = [[word for word in re.sub(\\\"[^a-zA-Z]\\\", \\\" \\\", doc.lower()) if word != \\\"\\\" and word not in list_stopWords] for doc in data_all]\\n\",\n    \"dictionary = corpora.Dictionary(texts)\\n\",\n    \"# 对每一行的单词，进行统计出现次数\\n\",\n    \"corpus = [dictionary.doc2bow(text) for text in texts]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0  \\n\",\n      \"1 b\\n\",\n      \"2 c\\n\",\n      \"3 e\\n\",\n      \"4 f\\n\",\n      \"[(0, 471), (1, 29), (2, 56), (3, 206), (4, 37), (5, 42), (6, 102), (7, 19), (8, 18), (9, 82), (10, 115), (11, 31), (12, 3), (13, 72), (14, 53), (15, 15), (16, 41), (17, 3), (18, 1)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for key in dictionary.keys()[0:5]:\\n\",\n    \"    print (key, dictionary[key])\\n\",\n    \"\\n\",\n    \"print(corpus[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.TF-IDF + Lsi主题模型\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from gensim import models\\n\",\n    \"tfidf_model = models.TfidfModel(corpus=corpus, id2word=dictionary, normalize=True) \\n\",\n    \"# 将整个corpus转为tf-idf格式\\n\",\n    \"corpus_tfidf = tfidf_model[corpus]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"## lsi 主题模型，作为特征向量\\n\",\n    \"lsi_model = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=200)\\n\",\n    \"corpus_lsi = lsi_model[corpus_tfidf]\\n\",\n    \"\\n\",\n    \"# 提取数字，转化为numpy的矩阵\\n\",\n    \"all_x = [[v for k,v in doc] for doc in corpus_lsi]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[(0, '0.162*\\\"movie\\\" + 0.140*\\\"film\\\" + 0.102*\\\"-\\\" + 0.099*\\\"good\\\" + 0.099*\\\"like\\\" + 0.098*\\\"really\\\" + 0.098*\\\"bad\\\" + 0.092*\\\"one\\\" + 0.089*\\\"would\\\" + 0.088*\\\"story\\\"'), (1, '0.276*\\\"bad\\\" + 0.238*\\\"movie\\\" + 0.180*\\\"worst\\\" + -0.156*\\\"-\\\" + 0.153*\\\"movies\\\" + 0.113*\\\"waste\\\" + 0.108*\\\"ever\\\" + 0.106*\\\"acting\\\" + 0.106*\\\"terrible\\\" + -0.101*\\\"film\\\"'), (2, '-0.667*\\\"show\\\" + -0.212*\\\"episode\\\" + -0.203*\\\"series\\\" + 0.160*\\\"-\\\" + 0.153*\\\"film\\\" + -0.146*\\\"season\\\" + -0.145*\\\"episodes\\\" + -0.135*\\\"tv\\\" + -0.130*\\\"shows\\\" + -0.125*\\\"funny\\\"')]\\n\",\n      \"(50000, 200)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# print(np.shape(corpus_lsi))\\n\",\n    \"# (50000, 200, 2)\\n\",\n    \"print(lsi_model.print_topics(3))\\n\",\n    \"# print(corpus_lsi[0])\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"print(np.shape(all_x))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.Word2vec向量\\n\",\n    \"\\n\",\n    \"神经网络语言模型 L = SUM[log(p(w|contect(w))]，即在w的上下文下计算当前词w的概率，由公式可以看到，我们的核心是计算p(w|contect(w)， Word2vec给出了构造这个概率的一个方法。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import re\\n\",\n    \"from bs4 import BeautifulSoup\\n\",\n    \"from nltk.corpus import stopwords\\n\",\n    \"\\n\",\n    \"# def show_diff(origin, html, text):\\n\",\n    \"#     print(origin)\\n\",\n    \"#     print(\\\"\\\\n-----------show diff-----------\\\\n\\\")\\n\",\n    \"#     print(html)\\n\",\n    \"#     print(\\\"\\\\n-----------show diff-----------\\\\n\\\")\\n\",\n    \"#     print(text)\\n\",\n    \"\\n\",\n    \"# origin = train['review'][0]\\n\",\n    \"# html = BeautifulSoup(origin, \\\"html.parser\\\").get_text()\\n\",\n    \"# text = re.sub('[^a-zA-Z]', ' ', html).strip()\\n\",\n    \"# show_diff(origin, html, text)\\n\",\n    \"\\n\",\n    \"stopwords = set(stopwords.words(\\\"english\\\"))\\n\",\n    \"\\n\",\n    \"def review_to_sentence(review):\\n\",\n    \"    html = BeautifulSoup(review, \\\"html.parser\\\").get_text()\\n\",\n    \"    text = re.sub('[^a-zA-Z]', ' ', html).strip()\\n\",\n    \"    words = [word for word in text.lower().split() if word not in stopwords]\\n\",\n    \"    return words\\n\",\n    \"\\n\",\n    \"unlabeled_train = pd.read_csv(os.path.join(data_dir, 'unlabeledTrainData.tsv'), header=0, delimiter=\\\"\\\\t\\\", quoting=3 )\\n\",\n    \"train_texts = pd.concat([train['review'], unlabeled_train['review']])\\n\",\n    \"sentences = list(map(review_to_sentence, train_texts))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(75000,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.shape(sentences))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"from gensim.models import Word2Vec\\n\",\n    \"# 模型参数\\n\",\n    \"num_features = 784    # Word vector dimensionality(原来默认用300维，为了计算CNN, 设置 784维 = 28*28)               \\n\",\n    \"min_word_count = 10   # Minimum word count                        \\n\",\n    \"num_workers = 4       # Number of threads to run in parallel\\n\",\n    \"context = 10          # Context window size                                                                                    \\n\",\n    \"downsampling = 1e-3   # Downsample setting for frequent words\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"训练模型中...\\n\",\n      \"训练完成\\n\",\n      \"CPU times: user 7min 17s, sys: 6.85 s, total: 7min 24s\\n\",\n      \"Wall time: 3min 1s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%time\\n\",\n    \"# 训练模型\\n\",\n    \"print(\\\"训练模型中...\\\")\\n\",\n    \"# model = Word2Vec(sentences, workers=num_workers, \\\\\\n\",\n    \"#             size=num_features, min_count=min_word_count, \\\\\\n\",\n    \"#             window=5, sample=downsampling)\\n\",\n    \"model = Word2Vec(sentences, size=num_features, window=5)\\n\",\n    \"print(\\\"训练完成\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\\n\",\n      \"  \\\"\\\"\\\"Entry point for launching an IPython kernel.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(784,)\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.shape(model[\\\"film\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'kitchen'\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.wv.doesnt_match(\\\"man woman child kitchen\\\".split())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'berlin'\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.wv.doesnt_match(\\\"france england germany berlin\\\".split())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'london'\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.wv.doesnt_match(\\\"paris berlin london austria\\\".split())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[('men', 0.5552874803543091),\\n\",\n       \" ('lady', 0.5526503920555115),\\n\",\n       \" ('woman', 0.49917668104171753),\\n\",\n       \" ('mans', 0.47213518619537354),\\n\",\n       \" ('guy', 0.4668915569782257)]\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.wv.most_similar(\\\"man\\\", topn=5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[('princess', 0.6967809200286865),\\n\",\n       \" ('bride', 0.6197341084480286),\\n\",\n       \" ('latifah', 0.6163896918296814),\\n\",\n       \" ('goddess', 0.6069524884223938),\\n\",\n       \" ('showgirl', 0.5752988457679749)]\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.wv.most_similar(\\\"queen\\\", topn=5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[('terrible', 0.8102608919143677),\\n\",\n       \" ('horrible', 0.7840115427970886),\\n\",\n       \" ('dreadful', 0.7728089690208435),\\n\",\n       \" ('horrid', 0.7526298761367798),\\n\",\n       \" ('atrocious', 0.7394574284553528)]\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.wv.most_similar(\\\"awful\\\", topn=5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[('princess', 0.4752192795276642)]\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.wv.most_similar(positive=['woman', 'king'], negative=['man'], topn=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def makeFeatureVec(words, model, num_features):\\n\",\n    \"    '''\\n\",\n    \"    对段落中的所有词向量进行取平均操作\\n\",\n    \"    '''\\n\",\n    \"    featureVec = np.zeros((num_features,), dtype=\\\"float32\\\")\\n\",\n    \"    nwords = 0.\\n\",\n    \"\\n\",\n    \"    # Index2word包含了词表中的所有词，为了检索速度，保存到set中\\n\",\n    \"    index2word_set = set(model.wv.index2word)\\n\",\n    \"    for word in words:\\n\",\n    \"        if word in index2word_set:\\n\",\n    \"            nwords = nwords + 1.\\n\",\n    \"            featureVec = np.add(featureVec, model[word])\\n\",\n    \"\\n\",\n    \"    # 取平均\\n\",\n    \"    featureVec = np.divide(featureVec, nwords)\\n\",\n    \"    return featureVec\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def getAvgFeatureVecs(reviews, model, num_features):\\n\",\n    \"    '''\\n\",\n    \"    给定一个文本列表，每个文本由一个词列表组成，返回每个文本的词向量加和的平均值\\n\",\n    \"    '''\\n\",\n    \"    counter = 0\\n\",\n    \"    reviewFeatureVecs = np.zeros((len(reviews), num_features), dtype=\\\"float32\\\")\\n\",\n    \"\\n\",\n    \"    for review in reviews:\\n\",\n    \"        if counter % 5000 == 0:\\n\",\n    \"            print(\\\"Review %d of %d\\\" % (counter, len(reviews)))\\n\",\n    \"\\n\",\n    \"        reviewFeatureVecs[counter] = makeFeatureVec(review, model, num_features)\\n\",\n    \"        counter = counter + 1\\n\",\n    \"\\n\",\n    \"    return reviewFeatureVecs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"CPU times: user 6 µs, sys: 1e+03 ns, total: 7 µs\\n\",\n      \"Wall time: 11.2 µs\\n\",\n      \"Review 0 of 25000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/ipykernel_launcher.py:13: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\\n\",\n      \"  del sys.path[0]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Review 5000 of 25000\\n\",\n      \"Review 10000 of 25000\\n\",\n      \"Review 15000 of 25000\\n\",\n      \"Review 20000 of 25000\\n\",\n      \"(25000, 784)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%time \\n\",\n    \"trainDataVecs = getAvgFeatureVecs(texts[:len_train], model, num_features)\\n\",\n    \"print(np.shape(trainDataVecs))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"CPU times: user 6 µs, sys: 2 µs, total: 8 µs\\n\",\n      \"Wall time: 97 µs\\n\",\n      \"Review 0 of 25000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/ipykernel_launcher.py:13: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\\n\",\n      \"  del sys.path[0]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Review 5000 of 25000\\n\",\n      \"Review 10000 of 25000\\n\",\n      \"Review 15000 of 25000\\n\",\n      \"Review 20000 of 25000\\n\",\n      \"(25000, 784)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%time \\n\",\n    \"testDataVecs = getAvgFeatureVecs(texts[len_train:], model, num_features)\\n\",\n    \"print(np.shape(testDataVecs))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"高斯贝叶斯分类器 10折交叉验证得分: \\n\",\n      \" [0.62715936 0.6181632  0.62577952 0.62458144 0.63289088 0.59956992\\n\",\n      \" 0.61033216 0.62668192 0.610296   0.60734944]\\n\",\n      \"\\n\",\n      \"高斯贝叶斯分类器 10折交叉验证平均得分: \\n\",\n      \" 0.618280384\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.naive_bayes import GaussianNB as GNB\\n\",\n    \"from sklearn.cross_validation import cross_val_score\\n\",\n    \"\\n\",\n    \"gnb_model = GNB()\\n\",\n    \"gnb_model.fit(trainDataVecs, label)\\n\",\n    \"\\n\",\n    \"scores = cross_val_score(gnb_model, trainDataVecs, label, cv=10, scoring='roc_auc')\\n\",\n    \"print(\\\"\\\\n高斯贝叶斯分类器 10折交叉验证得分: \\\\n\\\", scores)\\n\",\n    \"print(\\\"\\\\n高斯贝叶斯分类器 10折交叉验证平均得分: \\\\n\\\", np.mean(scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 机器学习 - 模型调参\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### KNN 模型训练\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"knn算法 10折交叉验证得分: \\n\",\n      \" [0.82005056 0.81503776 0.83006976 0.8199152  0.82069568 0.827304\\n\",\n      \" 0.81693088 0.8250944  0.80150176 0.821496  ]\\n\",\n      \"\\n\",\n      \"knn算法 10折交叉验证平均得分: \\n\",\n      \" 0.8198095999999999\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"\\n\",\n    \"knn_model = KNeighborsClassifier(n_neighbors=5)\\n\",\n    \"knn_model.fit(all_x[:len_train], label)\\n\",\n    \"\\n\",\n    \"scores = cross_val_score(knn_model, all_x[:len_train], label, cv=10, scoring='roc_auc')\\n\",\n    \"print(\\\"\\\\nknn算法 10折交叉验证得分: \\\\n\\\", scores)\\n\",\n    \"print(\\\"\\\\nknn算法 10折交叉验证平均得分: \\\\n\\\", np.mean(scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 决策树 模型训练\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"决策树 10折交叉验证得分: \\n\",\n      \" [0.7392 0.7232 0.7292 0.7236 0.7412 0.7164 0.718  0.724  0.7124 0.7156]\\n\",\n      \"\\n\",\n      \"决策树 10折交叉验证平均得分: \\n\",\n      \" 0.72428\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.tree import DecisionTreeClassifier\\n\",\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"\\n\",\n    \"tree_model = DecisionTreeClassifier()\\n\",\n    \"tree_model.fit(all_x[:len_train], label)\\n\",\n    \"\\n\",\n    \"scores = cross_val_score(tree_model, all_x[:len_train], label, cv=10, scoring='roc_auc')\\n\",\n    \"print(\\\"\\\\n决策树 10折交叉验证得分: \\\\n\\\", scores)\\n\",\n    \"print(\\\"\\\\n决策树 10折交叉验证平均得分: \\\\n\\\", np.mean(scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 逻辑回归 模型训练\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"逻辑回归 10折交叉验证得分: \\n\",\n      \" [0.94440064 0.94031744 0.95128192 0.94374784 0.9410656  0.94308864\\n\",\n      \" 0.94733184 0.948768   0.93660352 0.94612288]\\n\",\n      \"\\n\",\n      \"逻辑回归 10折交叉验证平均得分: \\n\",\n      \" 0.944272832\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"\\n\",\n    \"lr_model = LogisticRegression(C=0.1, max_iter=100)\\n\",\n    \"lr_model.fit(all_x[:len_train], label)\\n\",\n    \"\\n\",\n    \"scores = cross_val_score(lr_model, all_x[:len_train], label, cv=10, scoring='roc_auc')\\n\",\n    \"print(\\\"\\\\n逻辑回归 10折交叉验证得分: \\\\n\\\", scores)\\n\",\n    \"print(\\\"\\\\n逻辑回归 10折交叉验证平均得分: \\\\n\\\", np.mean(scores))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# from sklearn.model_selection import GridSearchCV\\n\",\n    \"\\n\",\n    \"# # 设定grid search的参数\\n\",\n    \"# grid_values = {'C': [1, 15, 30, 50]}  \\n\",\n    \"# \\\"\\\"\\\"\\n\",\n    \"# penalty: l1 or l2, 用于指定惩罚中使用的标准。\\n\",\n    \"# \\\"\\\"\\\"\\n\",\n    \"# model_LR = GridSearchCV(estimator=LR(penalty='l2', dual=True, random_state=0), grid_values, scoring='roc_auc', cv=20)\\n\",\n    \"# model_LR.fit(train_x, label)\\n\",\n    \"\\n\",\n    \"# 输出结果\\n\",\n    \"# print(model_LR.cv_results_, '\\\\n', model_LR.best_params_, model_LR.best_score_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### SVM 模型训练\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"SVM 10折交叉验证得分: \\n\",\n      \" [0.94539328 0.9421344  0.95242176 0.94563328 0.94189504 0.94408704\\n\",\n      \" 0.94839424 0.94898688 0.93809024 0.9473792 ]\\n\",\n      \"\\n\",\n      \"SVM 10折交叉验证平均得分: \\n\",\n      \" 0.945441536\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.svm import SVC\\n\",\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"\\n\",\n    \"# model = SVC(C=4, kernel='rbf')\\n\",\n    \"svm_model = SVC(kernel='linear', probability=True)\\n\",\n    \"svm_model.fit(all_x[:len_train], label)\\n\",\n    \"\\n\",\n    \"scores = cross_val_score(svm_model, all_x[:len_train], label, cv=10, scoring='roc_auc')\\n\",\n    \"print(\\\"\\\\nSVM 10折交叉验证得分: \\\\n\\\", scores)\\n\",\n    \"print(\\\"\\\\nSVM 10折交叉验证平均得分: \\\\n\\\", np.mean(scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 152,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"svm_model = SVC(kernel='linear', probability=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### XGBoost 模型训练\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"SVM 10折交叉验证得分: \\n\",\n      \" [0.93396416 0.93105024 0.93927616 0.9332768  0.9338336  0.93682432\\n\",\n      \" 0.93343296 0.93623296 0.92564352 0.93381504]\\n\",\n      \"\\n\",\n      \"SVM 10折交叉验证平均得分: \\n\",\n      \" 0.933734976\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"from xgboost import XGBClassifier\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"xgb_model = XGBClassifier(n_estimators=150, min_samples_leaf=3, max_depth=6)\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"AttributeError: 'list' object has no attribute 'shape'\\n\",\n    \"list => np.array\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"xgb_model.fit(np.array(all_x[:len_train]), label)\\n\",\n    \"\\n\",\n    \"scores = cross_val_score(xgb_model, np.array(all_x[:len_train]), label, cv=10, scoring='roc_auc')\\n\",\n    \"print(\\\"\\\\nXGB 10折交叉验证得分: \\\\n\\\", scores)\\n\",\n    \"print(\\\"\\\\nXGB 10折交叉验证平均得分: \\\\n\\\", np.mean(scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 模型融合\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### bagging: 随机森林 \\n\",\n    \"\\n\",\n    \"随机森林效果不好，去掉所有的树模型\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"随机森林 10折交叉验证得分: \\n\",\n      \" [0.94539328 0.9421344  0.95242176 0.94563328 0.94189504 0.94408704\\n\",\n      \" 0.94839424 0.94898688 0.93809024 0.9473792 ]\\n\",\n      \"\\n\",\n      \"随机森林 10折交叉验证平均得分: \\n\",\n      \" 0.945441536\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import GridSearchCV\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"\\n\",\n    \"# parameters= {'n_estimators': range(10, 101, 10)}  \\n\",\n    \"# gsearch_rf = GridSearchCV(\\n\",\n    \"#     estimator=RandomForestClassifier(max_depth=8, random_state=0),\\n\",\n    \"#     param_grid=parameters, scoring='roc_auc', cv=10)\\n\",\n    \"\\n\",\n    \"# gsearch_rf = gsearch_rf.fit(all_x[:len_train], label)\\n\",\n    \"\\n\",\n    \"# print(gsearch_rf.cv_results_, '\\\\n', gsearch_rf.best_params_, '\\\\t', gsearch_rf.best_score_)\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"[mean: 0.87486, std: 0.00576, params: {'n_estimators': 10}, mean: 0.88505, std: 0.00611, params: {'n_estimators': 20}, mean: 0.89032, std: 0.00609, params: {'n_estimators': 30}, mean: 0.89246, std: 0.00537, params: {'n_estimators': 40}, mean: 0.89439, std: 0.00528, params: {'n_estimators': 50}, \\n\",\n    \" mean: 0.89507, std: 0.00607, params: {'n_estimators': 60}, mean: 0.89591, std: 0.00618, params: {'n_estimators': 70}, mean: 0.89634, std: 0.00634, params: {'n_estimators': 80}, mean: 0.89671, std: 0.00607, params: {'n_estimators': 90}, mean: 0.89753, std: 0.00588, params: {'n_estimators': 100}] \\n\",\n    \"\\n\",\n    \"{'n_estimators': 100}  0.89753344\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"rf_model = RandomForestClassifier(n_estimators=100, max_depth=8, random_state=0)\\n\",\n    \"rf_model.fit(all_x[:len_train], label)\\n\",\n    \"\\n\",\n    \"scores = cross_val_score(svm_model, all_x[:len_train], label, cv=10, scoring='roc_auc')\\n\",\n    \"print(\\\"\\\\n随机森林 10折交叉验证得分: \\\\n\\\", scores)\\n\",\n    \"print(\\\"\\\\n随机森林 10折交叉验证平均得分: \\\\n\\\", np.mean(scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### boosting: AdaBoost\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"AdaBoost 10折交叉验证得分: \\n\",\n      \" [0.9207616  0.91974976 0.92787136 0.91906176 0.92305856 0.92228416\\n\",\n      \" 0.9224832  0.92169856 0.91816256 0.92096768]\\n\",\n      \"\\n\",\n      \"AdaBoost 10折交叉验证平均得分: \\n\",\n      \" 0.9216099200000001\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.ensemble import AdaBoostClassifier\\n\",\n    \"from sklearn.tree import DecisionTreeClassifier\\n\",\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"\\n\",\n    \"ab_model = AdaBoostClassifier(\\n\",\n    \"    DecisionTreeClassifier(max_depth=2),\\n\",\n    \"    n_estimators=600,\\n\",\n    \"    learning_rate=1)\\n\",\n    \"\\n\",\n    \"ab_model.fit(all_x[:len_train], label)\\n\",\n    \"\\n\",\n    \"scores = cross_val_score(ab_model, all_x[:len_train], label, cv=10, scoring='roc_auc')\\n\",\n    \"print(\\\"\\\\nAdaBoost 10折交叉验证得分: \\\\n\\\", scores)\\n\",\n    \"print(\\\"\\\\nAdaBoost 10折交叉验证平均得分: \\\\n\\\", np.mean(scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### voting: 多模型投票\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"from sklearn.ensemble import VotingClassifier\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"soft报错是因为这种投票方式使用的是每个分类器的概率输出值进行投票的。\\n\",\n    \"所以要求每个分类器的输出是概率值，而不是一个类别。\\n\",\n    \"对于svc来说，默认的输出是类别，所以会有问题，其他分类器不会有这样的问题。\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"vot_model = VotingClassifier(\\n\",\n    \"#     estimators=[('lr', lr_model), ('svm', svm_model), ('xgb', xgb_model), ('rf', rf_model), ('ab', ab_model)]\\n\",\n    \"    estimators=[('xgb', xgb_model), ('rf', rf_model)],\\n\",\n    \"    voting='hard')\\n\",\n    \"vot_model.fit(np.array(all_x[:len_train]), np.array(label))\\n\",\n    \"\\n\",\n    \"scores = cross_val_score(vot_model, np.array(all_x[:len_train]), np.array(label), cv=10, scoring='roc_auc')\\n\",\n    \"print(\\\"\\\\nAdaBoost 10折交叉验证得分: \\\\n\\\", scores)\\n\",\n    \"print(\\\"\\\\nAdaBoost 10折交叉验证平均得分: \\\\n\\\", np.mean(scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### stacking: 模型\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y = np.array([0, 0, 1, 1])\\n\",\n    \"skf = StratifiedKFold(y, 2)\\n\",\n    \"len(skf)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"'''模型融合中使用到的各个单模型'''\\n\",\n    \"import numpy as np\\n\",\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"from sklearn.cross_validation import StratifiedKFold\\n\",\n    \"\\n\",\n    \"# 划分train数据集,调用代码,把数据集名字转成和代码一样\\n\",\n    \"X = np.array(all_x[:len_train])\\n\",\n    \"X_predict = np.array(all_x[len_train:])\\n\",\n    \"label.astype(np.integer)\\n\",\n    \"y = label.values\\n\",\n    \"\\n\",\n    \"# clfs = [LogisticRegression(C=0.1, max_iter=100),\\n\",\n    \"#         xgb.XGBClassifier(max_depth=6, n_estimators=100, num_round = 5),\\n\",\n    \"#         RandomForestClassifier(n_estimators=100, max_depth=6, oob_score=True),\\n\",\n    \"#         GradientBoostingClassifier(learning_rate=0.3, max_depth=6, n_estimators=100)]\\n\",\n    \"\\n\",\n    \"clfs = [knn_model, tree_model, lr_model, svm_model, xgb_model, rf_model, ab_model]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# 创建n_folds\\n\",\n    \"n_folds = 10\\n\",\n    \"skf = StratifiedKFold(y, n_folds)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# 创建零矩阵\\n\",\n    \"dataset_blend_train = np.zeros((X.shape[0], len(clfs)))\\n\",\n    \"dataset_blend_test = np.zeros((X_predict.shape[0], len(clfs)))\\n\",\n    \"\\n\",\n    \"# 建立模型\\n\",\n    \"for j, clf in enumerate(clfs):\\n\",\n    \"    '''依次训练各个单模型'''\\n\",\n    \"    # print(j, clf)\\n\",\n    \"    dataset_blend_test_j = np.zeros((X_predict.shape[0], len(skf)))\\n\",\n    \"    for i, (train, test) in enumerate(skf):\\n\",\n    \"        '''使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。'''\\n\",\n    \"        # print(\\\"Fold\\\", i)\\n\",\n    \"        X_train, y_train, X_test, y_test = X[train], y[train], X[test], y[test]\\n\",\n    \"        clf.fit(X_train, y_train)\\n\",\n    \"        y_submission = clf.predict_proba(X_test)[:, 1]\\n\",\n    \"        dataset_blend_train[test, j] = y_submission\\n\",\n    \"        dataset_blend_test_j[:, i] = clf.predict_proba(X_predict)[:, 1]\\n\",\n    \"    '''对于测试集，直接用这k个模型的预测值均值作为新的特征。'''\\n\",\n    \"    dataset_blend_test[:, j] = dataset_blend_test_j.mean(1)\\n\",\n    \"\\n\",\n    \"# 用建立第二层模型\\n\",\n    \"stacking_model = LogisticRegression(C=0.1, max_iter=100)\\n\",\n    \"stacking_model.fit(dataset_blend_train, y_train)\\n\",\n    \"\\n\",\n    \"scores = cross_val_score(ab_model, dataset_blend_train, label, cv=10, scoring='roc_auc')\\n\",\n    \"print(\\\"\\\\nAdaBoost 10折交叉验证得分: \\\\n\\\", scores)\\n\",\n    \"print(\\\"\\\\nAdaBoost 10折交叉验证平均得分: \\\\n\\\", np.mean(scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 数据导出\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"保存结果...\\n\",\n      \"           id  sentiment\\n\",\n      \"0  \\\"12311_10\\\"          1\\n\",\n      \"1    \\\"8348_2\\\"          0\\n\",\n      \"2    \\\"5828_4\\\"          1\\n\",\n      \"3    \\\"7186_2\\\"          1\\n\",\n      \"4   \\\"12128_7\\\"          1\\n\",\n      \"5    \\\"2913_8\\\"          1\\n\",\n      \"6    \\\"4396_1\\\"          0\\n\",\n      \"7     \\\"395_2\\\"          0\\n\",\n      \"8   \\\"10616_1\\\"          0\\n\",\n      \"9    \\\"9074_9\\\"          1\\n\",\n      \"结束.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'\\\\n1.提交最终的结果到kaggle，AUC为：0.85728，排名300左右，50%的水平\\\\n2. ngram_range = 3, 三元文法，AUC为0.85924\\\\n'\"\n      ]\n     },\n     \"execution_count\": 70,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_predicted = np.array(model_NB.predict(corpus_tfidf[len_train:]))\\n\",\n    \"print('保存结果...')\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"root_dir = \\\"/opt/data/kaggle/getting-started/word2vec-nlp-tutorial\\\"\\n\",\n    \"        \\n\",\n    \"submission_df = pd.DataFrame(data ={'id': test['id'], 'sentiment': test_predicted})\\n\",\n    \"print(submission_df.head(10))\\n\",\n    \"submission_df.to_csv(os.path.join(root_dir, 'submission_br.csv'), index = False)\\n\",\n    \"\\n\",\n    \"print('结束.')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## CNN 来处理 文本问题： https://zhuanlan.zhihu.com/p/26729228\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 分词，获取分割后的所有文章\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import re\\n\",\n    \"from bs4 import BeautifulSoup\\n\",\n    \"from nltk.corpus import stopwords\\n\",\n    \"\\n\",\n    \"stopwords = set(stopwords.words(\\\"english\\\"))\\n\",\n    \"\\n\",\n    \"def review_to_sentence(review):\\n\",\n    \"    html = BeautifulSoup(review, \\\"html.parser\\\").get_text()\\n\",\n    \"    text = re.sub('[^a-zA-Z]', ' ', html).strip()\\n\",\n    \"    words = [word for word in text.lower().split() if word not in stopwords]\\n\",\n    \"    return words\\n\",\n    \"\\n\",\n    \"unlabeled_train = pd.read_csv(os.path.join(data_dir, 'unlabeledTrainData.tsv'), header=0, delimiter=\\\"\\\\t\\\", quoting=3 )\\n\",\n    \"train_texts = pd.concat([train['review'], unlabeled_train['review']], axis=0, ignore_index=True)\\n\",\n    \"sentences = list(map(review_to_sentence, train_texts))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"((75000,), 219, 84, 25000, 50000)\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.shape(sentences), len(sentences[0]), len(sentences[1]), len(train['review']), len( unlabeled_train['review'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 对文章简历词典\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from gensim import corpora\\n\",\n    \"dictionary = corpora.Dictionary(sentences)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dictionary.add_documents([[\\\" \\\"]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 actual\\n\",\n      \"1 alone\\n\",\n      \"2 also\\n\",\n      \"3 another\\n\",\n      \"4 anyway\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for key in dictionary.keys()[0:5]:\\n\",\n    \"    print (key, dictionary[key])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(123350, 123350, dict, dict)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(dictionary.token2id), len(dictionary.id2token), type(dictionary.token2id), type(dictionary.id2token)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(3, 'another', 123349)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dictionary.token2id[\\\"another\\\"], dictionary.id2token[3], dictionary.token2id[\\\" \\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F     # 激励函数都在这\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import torch.utils.data as Data\\n\",\n    \"import torchvision     # 数据库模块\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1416 \\n\",\n      \" 1416\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"max_len = max([len(i) for i in sentences])\\n\",\n    \"max_index = dictionary.token2id[\\\" \\\"]\\n\",\n    \"max_list = [max_index for x in range(max_len)]\\n\",\n    \"print(max_len, \\\"\\\\n\\\", len(max_list))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 362,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Variable containing:\\n\",\n      \"1.00000e-02 *\\n\",\n      \"  5.6823 -5.1981\\n\",\n      \"[torch.FloatTensor of size 1x2]\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# # prepare_sequence 是将文本的索引转化为 Variable 对象\\n\",\n    \"# def prepare_sequence(seq):\\n\",\n    \"#     idxs = [dictionary.token2id[w] for w in seq]\\n\",\n    \"#     if len(idxs) < max_len:\\n\",\n    \"#         idxs = idxs + max_list[len(idxs):]\\n\",\n    \"# #     print('文本词典的索引序列：', idxs)\\n\",\n    \"#     tensor = torch.LongTensor(idxs)\\n\",\n    \"#     return Variable(tensor)\\n\",\n    \"\\n\",\n    \"# sentence_in = prepare_sequence(sentences[1383])\\n\",\n    \"# # word_embeddings = nn.Embedding(len(dictionary.token2id), 5)\\n\",\n    \"# # word_embeddings(sentence_in)\\n\",\n    \"# x = cnn(sentence_in)\\n\",\n    \"# print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 319,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1416\"\n      ]\n     },\n     \"execution_count\": 319,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# sentence_in.data.size(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 320,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(['stuff', 'going', 'moment'], '\\\\n\\\\n', 1416)\"\n      ]\n     },\n     \"execution_count\": 320,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# sentences[0][:3], \\\"\\\\n\\\\n\\\", len(sentence_in)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 拆分数据集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# prepare_sequence 是将文本的索引转化为 Variable 对象\\n\",\n    \"def prepare_sequence(seq):\\n\",\n    \"    idxs = [dictionary.token2id[w] for w in seq]\\n\",\n    \"    if len(idxs) < max_len:\\n\",\n    \"        idxs = idxs + max_list[len(idxs):]\\n\",\n    \"#     print('文本词典的索引序列：', idxs)\\n\",\n    \"    return idxs\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"X_train = list(map(prepare_sequence, sentences[:len(train)]))\\n\",\n    \"X_train_d, X_test_d, y_train_d, y_test_d = train_test_split(X_train, label.tolist(), test_size=0.2, shuffle=True, random_state=42)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(list, list)\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(X_train_d), type(y_train_d)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"((20000, 1416), (5000, 1416), (20000,), (5000,))\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.shape(X_train_d), np.shape(X_test_d), np.shape(y_train_d), np.shape(y_test_d)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"([100, 1380, 2330], [509, 58, 14209], [0, 0, 0], [0, 1, 0])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_train_d[0][:3], X_test_d[0][:3], y_train_d[:3], y_test_d[:3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class textCNN(nn.Module):\\n\",\n    \"    \\n\",\n    \"    def __init__(self, vocab_size, embedding_dim, max_len, n_classes):\\n\",\n    \"        super(textCNN, self).__init__()\\n\",\n    \"        \\n\",\n    \"        self.model_name = 'alexnet'\\n\",\n    \"        self.vocab_size = vocab_size\\n\",\n    \"        self.embedding_dim = embedding_dim\\n\",\n    \"        self.max_len = max_len\\n\",\n    \"        \\n\",\n    \"        self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)\\n\",\n    \"        \\n\",\n    \"        self.features = nn.Sequential(\\n\",\n    \"#             nn.Conv2d(1, 64, kernel_size=11, stride=4, padding=2),\\n\",\n    \"#             nn.ReLU(inplace=True),\\n\",\n    \"#             nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"\\n\",\n    \"#             nn.Conv2d(64, 192, kernel_size=5, padding=2),\\n\",\n    \"#             nn.ReLU(inplace=True),\\n\",\n    \"#             nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"\\n\",\n    \"#             nn.Conv2d(192, 384, kernel_size=3, padding=1),\\n\",\n    \"#             nn.ReLU(inplace=True),\\n\",\n    \"\\n\",\n    \"#             nn.Conv2d(384, 256, kernel_size=3, padding=1),\\n\",\n    \"#             nn.ReLU(inplace=True),\\n\",\n    \"\\n\",\n    \"#             nn.Conv2d(256, 256, kernel_size=3, padding=1),\\n\",\n    \"#             nn.ReLU(inplace=True),\\n\",\n    \"#             nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"            \\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(1, 16, 5, 1, 2),\\n\",\n    \"            nn.ReLU(),    # activation\\n\",\n    \"            nn.MaxPool2d(kernel_size=2, stride=2),    # 在 2x2 空间里向下采样, output shape (16, 14, 14), 默认步长为2\\n\",\n    \"    \\n\",\n    \"            nn.Conv2d(16, 32, 5, 1, 2),  # output shape (32, 14, 14)\\n\",\n    \"            nn.ReLU(),  # activation\\n\",\n    \"            nn.MaxPool2d(2),  # output shape (32, 7, 7)\\n\",\n    \"\\n\",\n    \"            nn.Conv2d(32, 64, 5, 1, 2),  \\n\",\n    \"            nn.ReLU(),  \\n\",\n    \"            nn.MaxPool2d(2), \\n\",\n    \"    \\n\",\n    \"            nn.Conv2d(64, 128, 5, 1, 2),  \\n\",\n    \"            nn.ReLU(),  \\n\",\n    \"            nn.MaxPool2d(2)\\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.classifier = nn.Sequential(\\n\",\n    \"#             nn.Dropout(),\\n\",\n    \"#             nn.Linear(256 * 6 * 6, 4096),\\n\",\n    \"#             nn.ReLU(inplace=True),\\n\",\n    \"\\n\",\n    \"#             nn.Dropout(),\\n\",\n    \"#             nn.Linear(4096, 4096),\\n\",\n    \"#             nn.ReLU(inplace=True),\\n\",\n    \"\\n\",\n    \"#             nn.Linear(4096, n_classes),\\n\",\n    \"            \\n\",\n    \"            nn.Dropout(),\\n\",\n    \"            nn.Linear(45056, n_classes),   \\n\",\n    \"        )\\n\",\n    \" \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.word_embeddings(x)\\n\",\n    \"        x = x.view(len(x), 1, self.max_len, self.embedding_dim)\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        x = x.view(x.size(0), -1)\\n\",\n    \"        output = self.classifier(x)\\n\",\n    \"        return output     # return x for visualization\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"textCNN(\\n\",\n      \"  (word_embeddings): Embedding(123350, 64)\\n\",\n      \"  (features): Sequential(\\n\",\n      \"    (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\\n\",\n      \"    (1): ReLU()\\n\",\n      \"    (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)\\n\",\n      \"    (3): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\\n\",\n      \"    (4): ReLU()\\n\",\n      \"    (5): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)\\n\",\n      \"    (6): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\\n\",\n      \"    (7): ReLU()\\n\",\n      \"    (8): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)\\n\",\n      \"    (9): Conv2d(64, 128, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\\n\",\n      \"    (10): ReLU()\\n\",\n      \"    (11): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)\\n\",\n      \"  )\\n\",\n      \"  (classifier): Sequential(\\n\",\n      \"    (0): Dropout(p=0.5)\\n\",\n      \"    (1): Linear(in_features=45056, out_features=2, bias=True)\\n\",\n      \"  )\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cnn = textCNN(len(dictionary.token2id), 64, max_len, 2)\\n\",\n    \"print(cnn)  # net architecture\\n\",\n    \"\\n\",\n    \"# optimizer = torch.optim.SGD(cnn.parameters(), lr=0.02)  # 传入 net 的所有参数, 学习率\\n\",\n    \"# lr 优化步长\\n\",\n    \"# weight_decay(权重衰减): 也叫 L2 regularization  (1e-5就是 1*(10的-5次方)即0.00001)\\n\",\n    \"optimizer = torch.optim.Adam(cnn.parameters(), lr=1e-5, weight_decay=1e-7)\\n\",\n    \"# 算误差的时候, 注意真实值!不是! one-hot 形式的, 而是1D Tensor, (batch,)\\n\",\n    \"# 但是预测值是2D tensor (batch, n_classes)\\n\",\n    \"loss_func = nn.CrossEntropyLoss()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"import math\\n\",\n    \"\\n\",\n    \"def timeSince(since):\\n\",\n    \"    now = time.time()\\n\",\n    \"    s = now - since\\n\",\n    \"    m = math.floor(s / 60)\\n\",\n    \"    s -= m * 60\\n\",\n    \"    return '%dm %ds' % (m, s)\\n\",\n    \"\\n\",\n    \"start = time.time()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tr_x = torch.LongTensor(X_train_d)\\n\",\n    \"tr_y = torch.LongTensor(y_train_d)\\n\",\n    \"te_x = torch.LongTensor(X_test_d)\\n\",\n    \"te_y = torch.LongTensor(y_test_d)\\n\",\n    \"\\n\",\n    \"torch_train_dataset = Data.TensorDataset(data_tensor=tr_x, target_tensor=tr_y)\\n\",\n    \"torch_test_dataset = Data.TensorDataset(data_tensor=te_x, target_tensor=te_y)\\n\",\n    \"\\n\",\n    \"BATCH_SIZE = 20      # 批训练的数据个数\\n\",\n    \"\\n\",\n    \"# 把 dataset 放入 DataLoader\\n\",\n    \"train_loader = Data.DataLoader(\\n\",\n    \"    dataset=torch_train_dataset,      # torch TensorDataset format\\n\",\n    \"    batch_size=BATCH_SIZE,            # 每个 batch 加载多少个样本\\n\",\n    \"    shuffle=True,                     # 要不要打乱数据 (打乱比较好)\\n\",\n    \"    num_workers=2,                    # 多线程来读数据\\n\",\n    \")\\n\",\n    \"test_loader = Data.DataLoader(\\n\",\n    \"    dataset=torch_test_dataset,       # torch TensorDataset format\\n\",\n    \"    batch_size=BATCH_SIZE,            # 每个 batch 加载多少个样本\\n\",\n    \"    shuffle=True,                     # 要不要打乱数据 (打乱比较好)\\n\",\n    \"    num_workers=2,                    # 多线程来读数据\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"pred_y:\\t [1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1]\\n\",\n      \"target_y:\\t [0 1 1 1 0 1 0 0 1 0 0 1 1 1 1 0 1 0 1 0]\\n\",\n      \"0-19 7.60% (101m 42s) logloss=12.09 \\t accuracy=0.65 \\t loss=0.6844480037689209\\n\",\n      \"pred_y:\\t [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\\n\",\n      \"target_y:\\t [1 1 0 1 1 0 1 1 0 0 0 0 0 1 1 0 1 1 1 0]\\n\",\n      \"0-39 15.60% (103m 24s) logloss=15.54 \\t accuracy=0.55 \\t loss=0.6886069774627686\\n\",\n      \"pred_y:\\t [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\\n\",\n      \"target_y:\\t [0 1 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 0 0 1]\\n\",\n      \"0-59 23.60% (106m 31s) logloss=12.09 \\t accuracy=0.65 \\t loss=0.6793051958084106\\n\",\n      \"pred_y:\\t [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\\n\",\n      \"target_y:\\t [0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 1 0 0 1]\\n\",\n      \"0-79 31.60% (114m 37s) logloss=22.45 \\t accuracy=0.35 \\t loss=0.6999103426933289\\n\",\n      \"pred_y:\\t [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\\n\",\n      \"target_y:\\t [0 0 0 0 0 1 1 0 1 0 1 1 0 1 1 0 0 0 1 0]\\n\",\n      \"0-99 39.60% (120m 29s) logloss=20.72 \\t accuracy=0.40 \\t loss=0.7069584131240845\\n\",\n      \"pred_y:\\t [1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\\n\",\n      \"target_y:\\t [1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1]\\n\",\n      \"0-119 47.60% (125m 5s) logloss=19.00 \\t accuracy=0.45 \\t loss=0.7006260752677917\\n\",\n      \"pred_y:\\t [0 0 0 1 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1]\\n\",\n      \"target_y:\\t [0 1 1 0 0 1 0 0 1 1 0 0 0 0 1 1 1 1 1 0]\\n\",\n      \"0-139 55.60% (132m 49s) logloss=25.90 \\t accuracy=0.25 \\t loss=0.7005825638771057\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \"target_y:\\t [0 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 0 1 1 0]\\n\",\n      \"0-159 63.60% (140m 33s) logloss=24.18 \\t accuracy=0.30 \\t loss=0.699988067150116\\n\",\n      \"pred_y:\\t [1 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0]\\n\",\n      \"target_y:\\t [1 1 1 1 0 1 0 0 0 0 1 0 0 0 1 1 1 0 1 1]\\n\",\n      \"0-179 71.60% (148m 14s) logloss=15.54 \\t accuracy=0.55 \\t loss=0.689541220664978\\n\",\n      \"pred_y:\\t [1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 0 1 0 1 1]\\n\",\n      \"target_y:\\t [0 1 0 0 1 0 0 0 0 1 1 1 0 0 1 1 1 1 0 1]\\n\",\n      \"0-199 79.60% (154m 37s) logloss=19.00 \\t accuracy=0.45 \\t loss=0.6907564401626587\\n\",\n      \"pred_y:\\t [1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1]\\n\",\n      \"target_y:\\t [0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 0 1 1 0 0]\\n\",\n      \"0-219 87.60% (160m 44s) logloss=24.18 \\t accuracy=0.30 \\t loss=0.6987239122390747\\n\",\n      \"pred_y:\\t [1 0 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1]\\n\",\n      \"target_y:\\t [0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 1]\\n\",\n      \"0-239 95.60% (167m 26s) logloss=19.00 \\t accuracy=0.45 \\t loss=0.691254734992981\\n\",\n      \"pred_y:\\t [0 1 1 0 1 0 0 1 1 0 1 0 1 0 1 0 1 1 1 1]\\n\",\n      \"target_y:\\t [1 0 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1 0 0 0]\\n\",\n      \"0-259 103.60% (174m 38s) logloss=13.82 \\t accuracy=0.60 \\t loss=0.6896542310714722\\n\",\n      \"pred_y:\\t [0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0]\\n\",\n      \"target_y:\\t [1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 1]\\n\",\n      \"0-279 111.60% (181m 56s) logloss=12.09 \\t accuracy=0.65 \\t loss=0.6853312253952026\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0]\\n\",\n      \"target_y:\\t [1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1]\\n\",\n      \"0-299 119.60% (188m 49s) logloss=25.90 \\t accuracy=0.25 \\t loss=0.7102211713790894\\n\",\n      \"pred_y:\\t [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \"target_y:\\t [0 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0 1 1 1]\\n\",\n      \"0-319 127.60% (196m 8s) logloss=12.09 \\t accuracy=0.65 \\t loss=0.6825012564659119\\n\",\n      \"pred_y:\\t [0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 0 0]\\n\",\n      \"target_y:\\t [0 0 0 1 0 1 1 0 0 1 1 0 1 1 0 0 0 0 1 0]\\n\",\n      \"0-339 135.60% (203m 21s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.693179190158844\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0]\\n\",\n      \"target_y:\\t [0 1 0 1 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 1]\\n\",\n      \"0-359 143.60% (210m 48s) logloss=13.82 \\t accuracy=0.60 \\t loss=0.6911899447441101\\n\",\n      \"pred_y:\\t [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \"target_y:\\t [0 0 1 0 0 1 0 0 0 1 1 1 0 0 0 0 0 1 0 1]\\n\",\n      \"0-379 151.60% (218m 11s) logloss=10.36 \\t accuracy=0.70 \\t loss=0.6836111545562744\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \"target_y:\\t [0 1 1 1 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0]\\n\",\n      \"0-399 159.60% (225m 11s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.6962472200393677\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]\\n\",\n      \"target_y:\\t [0 1 1 0 0 1 0 0 0 1 1 0 0 0 1 1 0 0 1 1]\\n\",\n      \"0-419 167.60% (232m 36s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.6901986598968506\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1]\\n\",\n      \"target_y:\\t [1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1]\\n\",\n      \"0-439 175.60% (240m 1s) logloss=8.63 \\t accuracy=0.75 \\t loss=0.6787502765655518\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \"target_y:\\t [0 1 0 1 1 0 0 0 0 0 1 0 1 1 0 0 0 1 0 1]\\n\",\n      \"0-459 183.60% (247m 31s) logloss=13.82 \\t accuracy=0.60 \\t loss=0.694130003452301\\n\",\n      \"pred_y:\\t [0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \"target_y:\\t [1 0 1 1 0 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0]\\n\",\n      \"0-479 191.60% (254m 5s) logloss=15.54 \\t accuracy=0.55 \\t loss=0.6915131211280823\\n\",\n      \"pred_y:\\t [0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0]\\n\",\n      \"target_y:\\t [0 0 1 1 1 0 0 0 1 1 1 1 1 1 0 1 0 1 0 1]\\n\",\n      \"0-499 199.60% (260m 32s) logloss=20.72 \\t accuracy=0.40 \\t loss=0.696361243724823\\n\",\n      \"pred_y:\\t [0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 1 1 0 1]\\n\",\n      \"target_y:\\t [0 0 0 0 0 1 0 0 1 0 0 1 1 0 1 0 0 0 1 1]\\n\",\n      \"0-519 207.60% (267m 39s) logloss=15.54 \\t accuracy=0.55 \\t loss=0.6885088086128235\\n\",\n      \"pred_y:\\t [0 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1]\\n\",\n      \"target_y:\\t [1 0 0 0 1 0 1 1 1 0 1 0 0 0 0 1 1 0 0 0]\\n\",\n      \"0-539 215.60% (275m 2s) logloss=19.00 \\t accuracy=0.45 \\t loss=0.6945030093193054\\n\",\n      \"pred_y:\\t [1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0]\\n\",\n      \"target_y:\\t [1 0 0 0 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 0]\\n\",\n      \"0-559 223.60% (281m 12s) logloss=13.82 \\t accuracy=0.60 \\t loss=0.6861685514450073\\n\",\n      \"pred_y:\\t [1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1]\\n\",\n      \"target_y:\\t [0 1 1 1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0]\\n\",\n      \"0-579 231.60% (287m 12s) logloss=22.45 \\t accuracy=0.35 \\t loss=0.6945004463195801\\n\",\n      \"pred_y:\\t [0 1 0 0 1 1 1 1 0 1 1 1 0 0 0 1 0 1 0 1]\\n\",\n      \"target_y:\\t [1 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 0 1]\\n\",\n      \"0-599 239.60% (293m 44s) logloss=19.00 \\t accuracy=0.45 \\t loss=0.6927602887153625\\n\",\n      \"pred_y:\\t [1 1 1 1 1 0 1 1 1 1 1 0 0 0 1 0 1 0 1 0]\\n\",\n      \"target_y:\\t [0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 1 1 0 0 1]\\n\",\n      \"0-619 247.60% (300m 38s) logloss=12.09 \\t accuracy=0.65 \\t loss=0.6841057538986206\\n\",\n      \"pred_y:\\t [1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1]\\n\",\n      \"target_y:\\t [1 1 0 1 0 1 1 1 1 0 0 1 0 0 0 1 0 1 0 0]\\n\",\n      \"0-639 255.60% (307m 59s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.6867891550064087\\n\",\n      \"pred_y:\\t [1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 1 1 0 1 0]\\n\",\n      \"target_y:\\t [1 0 1 0 0 1 1 1 0 1 0 0 1 0 0 0 1 1 0 0]\\n\",\n      \"0-659 263.60% (315m 15s) logloss=20.72 \\t accuracy=0.40 \\t loss=0.697279691696167\\n\",\n      \"pred_y:\\t [0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 0 0]\\n\",\n      \"target_y:\\t [1 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 1 1 0]\\n\",\n      \"0-679 271.60% (322m 12s) logloss=24.18 \\t accuracy=0.30 \\t loss=0.6973448991775513\\n\",\n      \"pred_y:\\t [1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1]\\n\",\n      \"target_y:\\t [1 1 1 1 0 1 1 0 0 0 1 1 1 1 1 0 0 0 0 0]\\n\",\n      \"0-699 279.60% (329m 20s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.693761944770813\\n\",\n      \"pred_y:\\t [0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0]\\n\",\n      \"target_y:\\t [1 0 1 1 0 1 0 1 0 1 1 0 0 0 1 0 1 1 1 1]\\n\",\n      \"0-719 287.60% (335m 57s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.6938427686691284\\n\",\n      \"pred_y:\\t [0 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0]\\n\",\n      \"target_y:\\t [1 0 1 0 0 1 1 0 0 1 1 0 0 1 0 0 1 0 1 0]\\n\",\n      \"0-739 295.60% (342m 53s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.6820307970046997\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \"target_y:\\t [0 1 1 1 1 1 1 1 0 0 1 0 1 0 0 1 1 0 0 0]\\n\",\n      \"0-759 303.60% (350m 7s) logloss=15.54 \\t accuracy=0.55 \\t loss=0.6916964650154114\\n\",\n      \"pred_y:\\t [0 1 0 1 0 0 0 1 0 1 1 1 0 0 0 0 1 0 0 0]\\n\",\n      \"target_y:\\t [1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 0 0 0 0]\\n\",\n      \"0-779 311.60% (357m 5s) logloss=20.72 \\t accuracy=0.40 \\t loss=0.6954394578933716\\n\",\n      \"pred_y:\\t [0 1 0 1 1 1 0 1 1 1 0 1 0 0 1 0 1 0 1 0]\\n\",\n      \"target_y:\\t [0 0 1 1 1 1 0 0 0 0 1 0 1 1 0 1 1 0 0 0]\\n\",\n      \"0-799 319.60% (364m 23s) logloss=20.72 \\t accuracy=0.40 \\t loss=0.7011473178863525\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 1 1 0 1 0]\\n\",\n      \"target_y:\\t [0 1 1 1 1 0 0 1 1 0 1 0 0 0 0 1 1 1 0 1]\\n\",\n      \"0-819 327.60% (371m 51s) logloss=19.00 \\t accuracy=0.45 \\t loss=0.6923590898513794\\n\",\n      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logloss=10.36 \\t accuracy=0.70 \\t loss=0.6834084987640381\\n\",\n      \"pred_y:\\t [1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 0 0 1 1]\\n\",\n      \"target_y:\\t [0 1 0 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 0]\\n\",\n      \"3-489 195.60% (867m 51s) logloss=22.45 \\t accuracy=0.35 \\t loss=0.6945611238479614\\n\",\n      \"pred_y:\\t [1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0]\\n\",\n      \"target_y:\\t [0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 1 0 1 0 0]\\n\",\n      \"3-509 203.60% (870m 3s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.6905189752578735\\n\",\n      \"pred_y:\\t [1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0]\\n\",\n      \"target_y:\\t [1 1 1 0 1 1 0 0 1 0 1 0 0 0 1 1 0 0 0 1]\\n\",\n      \"3-529 211.60% (872m 42s) logloss=13.82 \\t accuracy=0.60 \\t loss=0.6931790113449097\\n\",\n      \"pred_y:\\t [0 1 0 1 1 0 1 1 1 1 0 1 0 1 0 1 0 0 0 0]\\n\",\n      \"target_y:\\t [0 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 0]\\n\",\n      \"3-549 219.60% (874m 53s) logloss=15.54 \\t accuracy=0.55 \\t loss=0.6936815977096558\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0]\\n\",\n      \"target_y:\\t [0 0 0 0 1 1 1 0 1 1 1 0 0 1 1 1 1 0 0 1]\\n\",\n      \"3-569 227.60% (877m 4s) logloss=24.18 \\t accuracy=0.30 \\t loss=0.7091041803359985\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0]\\n\",\n      \"target_y:\\t [0 0 0 1 0 1 1 0 1 0 0 0 1 1 0 0 0 0 0 1]\\n\",\n      \"3-589 235.60% (880m 4s) logloss=8.63 \\t accuracy=0.75 \\t loss=0.6674402356147766\\n\",\n      \"pred_y:\\t [0 0 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1]\\n\",\n      \"target_y:\\t [0 1 0 0 0 0 1 0 1 1 0 1 1 1 1 0 1 0 1 0]\\n\",\n      \"3-609 243.60% (882m 57s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.6928107142448425\\n\",\n      \"pred_y:\\t [0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 1 1 0 0 0]\\n\",\n      \"target_y:\\t [0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 1 1 1 1]\\n\",\n      \"3-629 251.60% (885m 15s) logloss=19.00 \\t accuracy=0.45 \\t loss=0.7027646899223328\\n\",\n      \"pred_y:\\t [0 0 0 0 1 1 0 0 1 1 0 1 1 0 0 0 0 0 1 1]\\n\",\n      \"target_y:\\t [0 0 0 0 1 1 1 1 0 0 0 1 1 1 0 0 1 1 1 1]\\n\",\n      \"3-649 259.60% (887m 35s) logloss=12.09 \\t accuracy=0.65 \\t loss=0.6845759153366089\\n\",\n      \"pred_y:\\t [0 1 0 0 1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0]\\n\",\n      \"target_y:\\t [1 1 1 1 1 1 0 0 0 1 0 1 1 0 0 1 1 1 1 1]\\n\",\n      \"3-669 267.60% (890m 7s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.6864821314811707\\n\",\n      \"pred_y:\\t [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\\n\",\n      \"target_y:\\t [0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0]\\n\",\n      \"3-689 275.60% (892m 46s) logloss=25.90 \\t accuracy=0.25 \\t loss=0.7397301197052002\\n\",\n      \"pred_y:\\t [1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 0]\\n\",\n      \"target_y:\\t [1 0 1 1 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1 0]\\n\",\n      \"3-709 283.60% (895m 49s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.6964901685714722\\n\",\n      \"pred_y:\\t [1 0 0 1 0 0 0 1 1 0 0 1 0 1 1 1 0 1 1 0]\\n\",\n      \"target_y:\\t [1 0 1 0 1 1 0 0 1 0 0 1 0 0 1 1 0 1 1 1]\\n\",\n      \"3-729 291.60% (898m 44s) logloss=12.09 \\t accuracy=0.65 \\t loss=0.6655923128128052\\n\",\n      \"pred_y:\\t [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\\n\",\n      \"target_y:\\t [0 1 0 1 1 1 1 0 1 0 1 0 0 0 1 0 1 1 1 0]\\n\",\n      \"3-749 299.60% (901m 31s) logloss=15.54 \\t accuracy=0.55 \\t loss=0.7032931447029114\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"pred_y:\\t [1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1]\\n\",\n      \"target_y:\\t [0 1 0 1 1 0 1 0 1 1 0 1 0 1 0 0 0 0 0 0]\\n\",\n      \"3-769 307.60% (904m 20s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.6945109963417053\\n\",\n      \"pred_y:\\t [0 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 1 1]\\n\",\n      \"target_y:\\t [0 1 1 0 1 1 1 1 1 0 0 0 1 0 0 1 0 0 1 0]\\n\",\n      \"3-789 315.60% (907m 19s) logloss=13.82 \\t accuracy=0.60 \\t loss=0.6908108592033386\\n\",\n      \"pred_y:\\t [1 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1]\\n\",\n      \"target_y:\\t [1 1 1 0 0 1 0 1 1 0 0 1 0 0 1 0 1 1 1 1]\\n\",\n      \"3-809 323.60% (909m 46s) logloss=15.54 \\t accuracy=0.55 \\t loss=0.6973456144332886\\n\",\n      \"pred_y:\\t [0 0 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 1 0 1]\\n\",\n      \"target_y:\\t [1 0 0 1 1 1 1 0 0 0 0 0 1 1 1 0 1 0 1 1]\\n\",\n      \"3-829 331.60% (912m 25s) logloss=19.00 \\t accuracy=0.45 \\t loss=0.6773894429206848\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \"target_y:\\t [1 0 0 0 1 0 1 1 0 1 0 1 1 0 0 1 1 0 0 1]\\n\",\n      \"3-849 339.60% (914m 47s) logloss=15.54 \\t accuracy=0.55 \\t loss=0.6820051670074463\\n\",\n      \"pred_y:\\t [0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0]\\n\",\n      \"target_y:\\t [0 1 0 0 1 1 0 1 1 0 0 0 0 1 1 0 1 1 0 1]\\n\",\n      \"3-869 347.60% (920m 18s) logloss=13.82 \\t accuracy=0.60 \\t loss=0.6797436475753784\\n\",\n      \"pred_y:\\t [0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0]\\n\",\n      \"target_y:\\t [0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1]\\n\",\n      \"3-889 355.60% (923m 7s) logloss=10.36 \\t accuracy=0.70 \\t loss=0.6741222143173218\\n\",\n      \"pred_y:\\t [0 0 1 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0]\\n\",\n      \"target_y:\\t [0 1 1 0 0 0 1 0 0 0 1 0 1 1 0 1 0 1 1 1]\\n\",\n      \"3-909 363.60% (925m 22s) logloss=13.82 \\t accuracy=0.60 \\t loss=0.6847935914993286\\n\",\n      \"pred_y:\\t [1 1 0 1 1 0 1 0 0 0 1 0 1 0 1 0 0 1 1 1]\\n\",\n      \"target_y:\\t [0 0 0 0 1 1 1 0 0 1 0 1 1 0 1 1 0 0 1 1]\\n\",\n      \"3-929 371.60% (927m 38s) logloss=15.54 \\t accuracy=0.55 \\t loss=0.7047096490859985\\n\",\n      \"pred_y:\\t [0 0 1 1 0 0 0 0 1 0 0 1 0 1 0 0 1 0 1 0]\\n\",\n      \"target_y:\\t [0 0 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0]\\n\",\n      \"3-949 379.60% (935m 52s) logloss=17.27 \\t accuracy=0.50 \\t loss=0.6819921731948853\\n\",\n      \"pred_y:\\t [1 0 0 0 1 1 1 1 1 0 0 0 1 1 0 1 0 0 0 0]\\n\",\n      \"target_y:\\t [1 0 0 1 1 1 0 1 1 0 1 1 0 1 0 1 1 0 1 0]\\n\",\n      \"3-969 387.60% (944m 46s) logloss=12.09 \\t accuracy=0.65 \\t loss=0.6759116053581238\\n\",\n      \"pred_y:\\t [1 0 0 1 1 0 0 1 1 1 1 0 1 0 0 0 1 1 0 1]\\n\",\n      \"target_y:\\t [1 0 0 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0]\\n\",\n      \"3-989 395.60% (953m 30s) logloss=19.00 \\t accuracy=0.45 \\t loss=0.6978853344917297\\n\",\n      \"pred_y:\\t [1 0 1 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 1 0]\\n\",\n      \"target_y:\\t [0 1 0 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 0]\\n\",\n      \"3-1009 403.60% (1268m 54s) logloss=22.45 \\t accuracy=0.35 \\t loss=0.6969307065010071\\n\",\n      \"pred_y:\\t [0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0]\\n\",\n      \"target_y:\\t [0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1]\\n\",\n      \"3-1029 411.60% (1271m 31s) logloss=22.45 \\t accuracy=0.35 \\t loss=0.7028256058692932\\n\",\n      \"pred_y:\\t [1 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1]\\n\",\n      \"target_y:\\t [0 1 1 0 1 1 0 1 0 1 0 1 1 1 1 1 0 1 1 0]\\n\",\n      \"3-1049 419.60% (1273m 47s) logloss=22.45 \\t accuracy=0.35 \\t loss=0.7021511793136597\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Process Process-20:\\n\",\n      \"Process Process-19:\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\\\", line 258, in _bootstrap\\n\",\n      \"    self.run()\\n\",\n      \"  File \\\"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\\\", line 258, in _bootstrap\\n\",\n      \"    self.run()\\n\",\n      \"  File \\\"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\\\", line 93, in run\\n\",\n      \"    self._target(*self._args, **self._kwargs)\\n\",\n      \"  File \\\"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\\\", line 93, in run\\n\",\n      \"    self._target(*self._args, **self._kwargs)\\n\",\n      \"  File \\\"/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/torch/utils/data/dataloader.py\\\", line 50, in _worker_loop\\n\",\n      \"    r = index_queue.get()\\n\",\n      \"  File \\\"/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/torch/utils/data/dataloader.py\\\", line 50, in _worker_loop\\n\",\n      \"    r = index_queue.get()\\n\",\n      \"  File \\\"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/queues.py\\\", line 335, in get\\n\",\n      \"    res = self._reader.recv_bytes()\\n\",\n      \"  File \\\"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/queues.py\\\", line 334, in get\\n\",\n      \"    with self._rlock:\\n\",\n      \"  File \\\"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py\\\", line 216, in recv_bytes\\n\",\n      \"    buf = self._recv_bytes(maxlength)\\n\",\n      \"  File \\\"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py\\\", line 407, in _recv_bytes\\n\",\n      \"    buf = self._recv(4)\\n\",\n      \"  File \\\"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/synchronize.py\\\", line 96, in __enter__\\n\",\n      \"    return self._semlock.__enter__()\\n\",\n      \"  File \\\"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py\\\", line 379, in _recv\\n\",\n      \"    chunk = read(handle, remaining)\\n\",\n      \"KeyboardInterrupt\\n\",\n      \"KeyboardInterrupt\\n\"\n     ]\n    },\n    {\n     \"ename\": \"KeyboardInterrupt\",\n     \"evalue\": \"\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m                         Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-34-ff8086e75901>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m     14\\u001b[0m         \\u001b[0mb_y\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mVariable\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mbatch_y\\u001b[0m\\u001b[0;34m)\\u001b[0m   \\u001b[0;31m# batch y\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     15\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 16\\u001b[0;31m         \\u001b[0mout\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mcnn\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mb_x\\u001b[0m\\u001b[0;34m)\\u001b[0m                 \\u001b[0;31m# 喂给 net 训练数据 x, 输出分析值\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m     17\\u001b[0m         \\u001b[0mloss\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mloss_func\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mout\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mb_y\\u001b[0m\\u001b[0;34m)\\u001b[0m     \\u001b[0;31m# 计算两者的误差\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     18\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/.virtualenvs/python3.6/lib/python3.6/site-packages/torch/nn/modules/module.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, *input, **kwargs)\\u001b[0m\\n\\u001b[1;32m    355\\u001b[0m             \\u001b[0mresult\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_slow_forward\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0minput\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    356\\u001b[0m         \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 357\\u001b[0;31m             \\u001b[0mresult\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mforward\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0minput\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m    358\\u001b[0m         \\u001b[0;32mfor\\u001b[0m \\u001b[0mhook\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_forward_hooks\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mvalues\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    359\\u001b[0m             \\u001b[0mhook_result\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mhook\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0minput\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresult\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m<ipython-input-20-271f63e3d649>\\u001b[0m in \\u001b[0;36mforward\\u001b[0;34m(self, x)\\u001b[0m\\n\\u001b[1;32m     66\\u001b[0m         \\u001b[0mx\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mword_embeddings\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mx\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     67\\u001b[0m         \\u001b[0mx\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mx\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mview\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mx\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m 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70\\u001b[0m         \\u001b[0moutput\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclassifier\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mx\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/.virtualenvs/python3.6/lib/python3.6/site-packages/torch/nn/modules/module.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, *input, **kwargs)\\u001b[0m\\n\\u001b[1;32m    355\\u001b[0m             \\u001b[0mresult\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_slow_forward\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0minput\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    356\\u001b[0m         \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 357\\u001b[0;31m             \\u001b[0mresult\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mforward\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0minput\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m    358\\u001b[0m         \\u001b[0;32mfor\\u001b[0m \\u001b[0mhook\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_forward_hooks\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mvalues\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    359\\u001b[0m             \\u001b[0mhook_result\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mhook\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0minput\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresult\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/.virtualenvs/python3.6/lib/python3.6/site-packages/torch/nn/modules/container.py\\u001b[0m in \\u001b[0;36mforward\\u001b[0;34m(self, input)\\u001b[0m\\n\\u001b[1;32m     65\\u001b[0m     \\u001b[0;32mdef\\u001b[0m \\u001b[0mforward\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0minput\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     66\\u001b[0m         \\u001b[0;32mfor\\u001b[0m \\u001b[0mmodule\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_modules\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mvalues\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 67\\u001b[0;31m             \\u001b[0minput\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mmodule\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0minput\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m     68\\u001b[0m         \\u001b[0;32mreturn\\u001b[0m \\u001b[0minput\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     69\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/.virtualenvs/python3.6/lib/python3.6/site-packages/torch/nn/modules/module.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, *input, **kwargs)\\u001b[0m\\n\\u001b[1;32m    355\\u001b[0m             \\u001b[0mresult\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_slow_forward\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0minput\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    356\\u001b[0m         \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 357\\u001b[0;31m             \\u001b[0mresult\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mforward\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0minput\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m    358\\u001b[0m         \\u001b[0;32mfor\\u001b[0m \\u001b[0mhook\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_forward_hooks\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mvalues\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    359\\u001b[0m             \\u001b[0mhook_result\\u001b[0m \\u001b[0;34m=\\u001b[0m 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\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mthreshold\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mvalue\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0minplace\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m     44\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     45\\u001b[0m     \\u001b[0;32mdef\\u001b[0m \\u001b[0m__repr__\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m: \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import log_loss\\n\",\n    \"\\n\",\n    \"Epoch = 5\\n\",\n    \"print_every = 20\\n\",\n    \"max_step = len(X_train_d)/BATCH_SIZE\\n\",\n    \"\\n\",\n    \"# 跟踪绘图的损失\\n\",\n    \"current_loss = 0\\n\",\n    \"all_losses = []\\n\",\n    \"\\n\",\n    \"for epoch in range(Epoch):\\n\",\n    \"    for step, (batch_x,  batch_y) in enumerate(train_loader):  # 每一步 loader 释放一小批数据用来学习\\n\",\n    \"        b_x = Variable(batch_x)   # batch x\\n\",\n    \"        b_y = Variable(batch_y)   # batch y\\n\",\n    \"        \\n\",\n    \"        out = cnn(b_x)                 # 喂给 net 训练数据 x, 输出分析值\\n\",\n    \"        loss = loss_func(out, b_y)     # 计算两者的误差\\n\",\n    \"\\n\",\n    \"        optimizer.zero_grad()   # 清空上一步的残余更新参数值\\n\",\n    \"        loss.backward()         # 误差反向传播, 计算参数更新值\\n\",\n    \"        optimizer.step()        # 将参数更新值施加到 net 的 parameters 上\\n\",\n    \"\\n\",\n    \"        current_loss += loss.data[0]\\n\",\n    \"        # print(F.softmax(out), '---', torch.max(F.softmax(out), 1), 'xxx', torch.max(F.softmax(out), 1)[1])\\n\",\n    \"        if step % print_every == print_every-1:\\n\",\n    \"            # softmax 用来计算输出分类的概率，然后max是选出最大的一组：(概率值，分类值)\\n\",\n    \"            prediction = torch.max(F.softmax(out, dim=1), 1)[1]\\n\",\n    \"            pred_y = prediction.data.numpy().squeeze()\\n\",\n    \"            target_y = b_y.data.numpy()\\n\",\n    \"            print(\\\"pred_y:\\\\t\\\", pred_y)\\n\",\n    \"            print(\\\"target_y:\\\\t\\\", target_y)\\n\",\n    \"            logloss = log_loss(target_y, pred_y, eps=1e-15)\\n\",\n    \"            accuracy = sum(pred_y == target_y)/len(target_y)  # 预测中有多少和真实值一样\\n\",\n    \"\\n\",\n    \"            # 总次数\\n\",\n    \"            loop_step = epoch*max_step + step\\n\",\n    \"            total_step = Epoch*max_step\\n\",\n    \"            print('%d-%d %.2f%% (%s) logloss=%.2f \\\\t accuracy=%.2f \\\\t loss=%s' % (epoch, loop_step, loop_step/total_step*100, timeSince(start), logloss, accuracy, loss.data[0]))\\n\",\n    \"\\n\",\n    \"            all_losses.append(current_loss/print_every)\\n\",\n    \"            current_loss = 0\\n\",\n    \"     \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.metrics import log_loss\\n\",\n    \"\\n\",\n    \"Epoch = 1\\n\",\n    \"print_every = 100\\n\",\n    \"max_step = len(X_train_d)\\n\",\n    \"\\n\",\n    \"# 跟踪绘图的损失\\n\",\n    \"current_loss = 0\\n\",\n    \"all_losses = []\\n\",\n    \"pre_result = []\\n\",\n    \"rea_result = []\\n\",\n    \"\\n\",\n    \"for epoch in range(Epoch):\\n\",\n    \"    # 优化为批处理\\n\",\n    \"    \\n\",\n    \"    # step = len(X_train_d)/BATCH_SIZE (train_loader 为 BATCH_SIZE 大小的集合)\\n\",\n    \"    for step, (x, y) in enumerate(zip(X_train_d[1384:], y_train_d[1384:])):\\n\",\n    \"        b_x = prepare_sequence(x)\\n\",\n    \"        b_y = Variable(torch.LongTensor([y]))   # batch y\\n\",\n    \"        \\n\",\n    \"        out = cnn(b_x)                 # 喂给 net 训练数据 x, 输出分析值\\n\",\n    \"        loss = loss_func(out, b_y)     # 计算两者的误差\\n\",\n    \"\\n\",\n    \"        optimizer.zero_grad()   # 清空上一步的残余更新参数值\\n\",\n    \"        loss.backward()         # 误差反向传播, 计算参数更新值\\n\",\n    \"        optimizer.step()        # 将参数更新值施加到 net 的 parameters 上\\n\",\n    \"\\n\",\n    \"        current_loss += loss.data[0]\\n\",\n    \"\\n\",\n    \"        prediction = torch.max(F.softmax(out, dim=1), 1)[1]\\n\",\n    \"        pred_y = prediction.data.numpy()\\n\",\n    \"        target_y = b_y.data.numpy()\\n\",\n    \"        \\n\",\n    \"#         print('预测---', pred_y)\\n\",\n    \"#         print('目标---', target_y)\\n\",\n    \"            \\n\",\n    \"#         if step>2:\\n\",\n    \"#             break\\n\",\n    \"\\n\",\n    \"        # softmax 用来计算输出分类的概率，然后max是选出最大的一组：(概率值，分类值)\\n\",\n    \"        prediction = torch.max(F.softmax(out, dim=1), 1)[1]\\n\",\n    \"        pre_result.append(prediction.data.numpy()[0])\\n\",\n    \"        rea_result.append(b_y.data.numpy()[0])\\n\",\n    \"        \\n\",\n    \"        # print(F.softmax(out), '---', torch.max(F.softmax(out), 1), 'xxx', torch.max(F.softmax(out), 1)[1])\\n\",\n    \"        if step % print_every == print_every-1:\\n\",\n    \"#             print('预测---', pred_y)\\n\",\n    \"#             print('目标---', target_y)\\n\",\n    \"            logloss = log_loss(rea_result, pre_result, eps=1e-15)\\n\",\n    \"#             print(\\\"pre_result: \\\\t\\\", pre_result)\\n\",\n    \"#             print(\\\"rea_result: \\\\t\\\", rea_result)\\n\",\n    \"            accuracy = sum(np.array(pre_result) == np.array(rea_result))/len(rea_result)  # 预测中有多少和真实值一样\\n\",\n    \"            \\n\",\n    \"            # 总次数\\n\",\n    \"            loop_step = epoch*max_step + step\\n\",\n    \"            total_step = Epoch*max_step\\n\",\n    \"            \\n\",\n    \"            print('%d-%d %.2f%% (%s) logloss=%.2f \\\\t accuracy=%.2f \\\\t loss=%s' % (epoch, loop_step, loop_step/total_step*100, timeSince(start), logloss, accuracy, loss.data[0]))\\n\",\n    \"\\n\",\n    \"            all_losses.append(current_loss/print_every)\\n\",\n    \"            current_loss = 0\\n\",\n    \"            pre_result = []\\n\",\n    \"            rea_result = []\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"一，train loss与test loss结果分析\\n\",\n    \"\\n\",\n    \"* train loss 不断下降，test loss不断下降，说明网络仍在学习;\\n\",\n    \"* train loss 不断下降，test loss趋于不变，说明网络过拟合;\\n\",\n    \"* train loss 趋于不变，test loss不断下降，说明数据集100%有问题;\\n\",\n    \"* train loss 趋于不变，test loss趋于不变，说明学习遇到瓶颈，需要减小学习率或批量数目;\\n\",\n    \"* train loss 不断上升，test loss不断上升，说明网络结构设计不当，训练超参数设置不当，数据集经过清洗等问题。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x15e174080>]\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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1P4Uf++BsjfZ8+wLebHqoCwjoK03SGsBkZIXT87SgZPTnfGjn7iRZCK90QK90Q4zUPnsMKV8TzRQwhToO667DczWo2A4O6nUY5AYFeG2bhUbau2OZo5UlGG+IhpJLRE8+u4I1/egdulznZmwGa9A4tdPDv//bbQ33mwWNLePDY0kiD32QI4vOdIMJP/+Xd2C8zS84UMgxhnR7CZngkbRUQthdDOHSyjdd96Gu4Q6aLrhX03C+vCoYwXvPguU7hPSTJaDWMDWauMwTXYVvWVVdfuecyhBzJZy2b3pR1CGcQdx5YwNcen+/7nlzJKIzXnR+uJKMgxklJh0fd23cY0IDbPVXHXQdP5T7MB0+aGS3LqyE4H22VGuZkY9x/ZAn//OgJfHMTA98woEEnKpXdkSQjMyCcOwzh2JIo1By1mRwx45Qh+PAdVugBEEMATNmIJkxP9qPaqgl0EENQvYxys4yGr0PYbokY50RA+LOvPYkP3fJY3/fkmcqjTpI6aGCshvGWdM2kY8+OVdT36njw6BJe88Fb8cCRJfUadXUdxTPRc/tpQByYFwHnxPLW7XCVh0hbXY4qGbVltbrDNscjoeP3thlDoL0jRm33kXoIwlQer3lwHadw9awHBF02sj2EUXcoWyuGrVTWCzJHqUPYKpN8WJwTAaGfdgmIH7QVpNRd3+BivdXKNGF2glgdix6Y2/efVMxko0ADcaLuy+81z5/S++ZbXfXasgoIa5+UjMI0+d9Pzgup6MRKN/czScJxz6FTA49962MnVMPBUWCnnY5SmEZBfKLuI9gMyYieu21mKtMiYdTFS+ohRMpD8F1WmKk1v9LFeE00sNMDArFO8hC2iiEM2kLTKFyzisyG8xBgfGa74JwICO6AgNAKIvUDrVopgMPWD3z0tgN4x198K/O6qkMIY7UaDGOOVi/C2z92F/77PUeGvYyhQAN4UgYE+/wDVYWdPrSUFjhKQNBXkHSP04CQzxC++eQC/tc/uwOPD5jsf/0fH8BHbzvY9z39QHNPmna69utrBSJDpu6762II7V6E137oVtxtBUK9tfZ2SkFcf0AQ93p5NZQegt93HM6v9PD88ycApIsWIOshbFV7FLMlRfYe5AWMtVQflx7CGcSglYW+164uGQHDM4SHjy3jXlmRqUNPO9V74nTDGAkHTm/wJjykSU7JfYTbVoM+Wr3oUtJ6JCOTIVgBoUAyOr0qVoB2RomNXpSsaxtTWrmtp7lduxehWXXhu866Bu+zy10cmG9ngqDuUWwnY5meiVFZkZ12qpvKti/HOcd8q4crzx8HYDMELcuIbSOGkGcmr6kwTX5PzntvfugZQ0LbSpwjAcHpu7LQZZtVWancqIjeKe1ejH/6zlE8s5QvfxB6UYJ2L8o87CrLSJOMgihRD81GZDLpyDCE0Dy+nvUEiMFIktEo1dQ2Q+iGMY4sCkOyiCF0tXvS99hxsq6W06TPOpqHsNYkgU4vRrPqwXPZyN1SgTQw20VLLS1gDxMQfuvGh/C3dx0e+TyGxdI6PQRaCC2thmj1IkzUfHgOA5BOhoTTnRBhzHHJbBNVzynwEBy4bn+mDwD7T6zgO0+fHumcdQxrKgPpwoNeG6rbKfkN1rHbvQj/+9/ci7d/7C7VTmcrMVRAYIzdwBh7jDG2nzH23oL3vIkx9jBj7CHG2Ce113+XMfag/OfNOZ/7I8bYpuYnuqw/NSOG0Ky46IWiDmG6IUzZ+ZUu/uOnvzNwEPYiseK3Teg8UzlKuKKVG1HroCOwAkIRQ+gGaX0E3ZuRJCO9DiFOcGihDc6By2abWGj3cldAw25AFMbJulpO01e70kNI+NopeqsXoVnx4DvO+iSjIP3tjdd1hjDEtX75kWfxjSc2P3uLMuHW6yEcOy0WB4IhsNxj0qY5O8ermGlWVDYeYNYheI4zMCB88ObH8d5/uH+kc9Yx0FTWPYTYlIqGyRwqqlSmsfHoMyt4+8e+hT+5db+6h1uBgQGBMeYC+DCAHwKwD8BbGWP7rPfsBfBrAF7FOX8BgF+Ur78ewIsBXAvg5QB+hTE2oX3uOgDTG3MpxXAHPEjEEHZO1JRkNCOzdJ4cMmOGBkDLWvHrnVNp8g+jRE2k7Q1mCPRQTtXF+duTru5pAOa+0aPsRhVECXw50KOE48kT4n69/LIZcJ6ftpga7f2vPYz5uiQCvQ6BeuFQb51h5Zl2ICUjjw3Uhv/01ifxL09lZUMgvVZ79bhWySiME/Wb3vTAcfz9vakHxTnHBz7/MO4/sv4V8kZ5CJS+qjMEeyySPDI3XsWOsQpOtXM8BJfBYYNbPXTCGKc7619Z5xWc6Uj6MIRhFg5FvYwomHzv3lk8u9TFf/3iY/jTW59c49mPjmEYwssA7OecH+CcBwA+DeAN1nveCeDDnPNFAOCcn5Cv7wPwdc55xDlvA7gfwA2ACjS/B+BX138Z/TEoy4gYwtx4VZnKO5oyIJzonzFDICnGnuD1XkZ62qmSjDZ4VzY6blGWke0h6LR0FPkqiBPUfSGvRUmi/INXXLYDQH4g7VpBKQ9xIkzW9aTo2oVpgAgIb/zTb+K//fP+oY7RJsnIGexB/MGXH8ff3f107t9IGrKDSmuNASGKuZLaPnHnYXz0tgPa5xP8+W0H8ZVHThR9PIPFdoCbH3om8/p6JSP63PHTYtyISmVHXYMOGltz41XsaFYLs4w8xxlY2RtGyYZILXktKYy/57THHmk/BOte0GffcO1u3Pnrr8Nls00sdrZmH2lguICwG4D+lB+Rr+m4AsAVjLHbGWN3MsZukK/fB+AGxliDMTYL4DUALpR/exeAGznnx0c//eHgDDCVl/WAEIr9EFRAGJAxQ6C0wQxDCLOSUZhw1RfHlnTWC3qgyFTOMgRTv1/SVlOjpp02KiJdMIo5npxvYfdUHRftaADID6R6Ku6g6+g3uO45dCpzv3Wkze3S9slBnODYUhdPD9nrqU2SkTuYIYRxovwTG52CGhSTIQyeSMI4QUf6Qu0gNhjeSo+M4OEn8T/72pP4uU/cm1k4qDqEdUpGNO7Ga75iknbqKTGEneNVzDYrBqvMVCoPOJ1AMqj11voMu4Wm/t/0mWGMeF4gGdF9o3s1VvOMpJfNxkaZyh6AvQBeDeCtAP6cMTbFOb8FwE0AvgngUwDuABAzxnYB+HEAfzzowIyxn2WM3cMYu2d+vn+1ceHJDShoUZKRZAhBlGBHw5SMBrn+AxlCkBjGYrBJDIFWZoVpp+QhKIZgZliN8n1kwEcJx0IrwHkTVeycqAHID6S9ITwEGtBFk3AniPDmj9yJT9xZ7O3oG+RUXEd9ZxAlxkTaDyLLyJNZRsWTTJxwcA4cLdB7lVxoXY/uIQ1TnBYlXN231SAyroPar6zFd7njwEJuAea6JSPreFSYBmQnwaXVEK7DMFb1sKNZya1UVr2MBjGEOK1/WA/ihKtnJu87cwMCeQhDdTuV52sdm86fvnu85vVd9Gw0hgkIR5Gu6gFgj3xNxxGI1X7IOT8I4HGIAAHO+Qc459dyzq8HwOTfXgTgcgD7GWOHADQYY7kcnnP+Ec75dZzz6+bm5tZwaSlch/XV9Va6ETyHYapeUQ/gtGQI9GOcbPX6yk69Aoag5JEgMoxF+uE3+semCUeZykGxhAVYHsJIaaccdS0g9KIYNd/F3FgVQL5klAbJ4u8LlUGX/7strYaIE45DJ4s3lkkrlR3FEE53qH3I4IAQJxxLqyHGqi481+m78qPzPLq4misxFFWpt3uRGvzDFKdFMVcJAe1ejE4QZ569YVf1y90QDx4VFeu6NMQ5TwPCiB1e7XPoZyqHMYfvMjDGsGOsgtUwVgsZI8vIYRi0+FbpriOmcx9Z7KAbxiIg9NnHWb+E0SQj8V5761kVEOR3j1f9DS9e7YdhAsLdAPYyxi5ljFUAvAXAjdZ7PgvBDiCloSsAHGCMuYyxGfn6NQCuAXAL5/zznPPzOeeXcM4vAdDhnF++IVeUA9dhmVQ3AHhqoYM/+soTWF4VlZS00gWARsVV2jggIrq9eYeOgaZyGBsDNvUQ+k8Cf3/vEfyXLwy/vSIdt+o5qHpOYWEafS8NfMZGL0xTDCFO0IsSVD0xAU83/FzJaJgso1Qyyp8BaAVYJNEAWqWyk26wQobjMAzh7kOn0A5iXHfJDvgDFhV0vkGcqKwZHWoxkBMQKIFhkGTEOReSSEFiQKsg6BTh7oOn1LjQA4KeeTaKZBTFoqU3VR4DQjIqMpVFYoL4fWbkQuyUDNxrZQh0vqP4CEnC8UP/7234xJ2HkfA0IOQ11MurVFYb5AyRCKHfA50l0G/nawxhW0lGnPMIQu+/GcAjAD7DOX+IMfZ+xtiPyrfdDGCBMfYwgK8CeA/nfAGAD+A2+fpHALxNHm9LIfqoZx+kD391P37/S4/jq4+ewHjNR00LCDXfVRMdPcj9Mo1o4tc9gShOECUcNV+kPNJkFGp1CIOyjG556Bl87jvHhrlMcWx6oDwHjYqbwxDMPZ5pJTXTrI5WhxAnqGseQhAlav/bufFqrtQ2zJ7VSjIqmABo1VQk0QDpoNVNZTLoloZoMHjjfcdQ91287vk7hWTUlyGkf8vbi6IjnwubZbR6kfKrBpnKNIl0FEMQ10A+EElGw7bouPNA2sk0iLOFiuL1tQcE+n5iiQBUYRqQDfJRkgYEepZShiCORR7CIB8nZQij9eVa6UZYaAeI4lQyGtZDWMsGObqCrV9TIBlZGhD87RUQAIBzfhPn/ArO+fM45x+Qr72Pc36j/G/OOX8353wf5/xqzvmn5etd+do+zvkrOOffKTj+2EZdUB6cnCyjbhjjpgeFn31sSfRR0RlBzRcbegDAVReICsq8lR9B7cOsTfA0mCgFdFFb9dAP3w76d1Q9vRr2HeC/fdMj+JNbU7WNvrPiOmhUvKypbE3GS6shxqsexmvemmoiDi+0ld/S8HXJKEHVF4/VzvFarocwHEPon9NNg6RIogFMQ7LiinNclJOn6PDaXwL6wgPHcf2+89CoCLmj38pbX/nnsRZVg5JhCDFm5MQ5iCGoVXuUIIgS9VuezjCE4WSeOw4sgIm1jvHdekAYpQ6EzmtWXlfFdVDz3UKGEEZcmag1GbjpGTHrEAY3twvXwRCIcQVRgphzxUr6baGp//coze0AM+CkkpG4H+QhbFVbk3OiUjkv7fTWx+ax0o1w+U4RizIBwXPRlKuVa/ZMAQDmh2AIumRExhpl/NAzoEtGsZxEi7DUCfuuHL/w4HHc+mhqtofaCqNRcTOrcNtDWO6GmKj7qPtuX01fR5xwvP6PvoGP337QMpUT9MJYyTM7CxjCMGmng7KMKCAUSTR0nkDa3A5IPYQgTvre99v3n8RiJ8SPvHAXAHE/+22hGQwKCEGxhzA7JEPQP6unZhLzVJLkEF5ELxKb2r9g10Tm/JfXyRBolT47Lq5roi7GEQWEjIeQJColtSbHIDFZvQ5hmOZ2FAxH8RDoeQyl5NWvod6GBgTt/XTvdMkI2HivsQjnREBwmPAQ9BXhP33nKGbHqvjAj30XAEHNan56O3TJ6JrdkwCKaxFIGgJMhkATDhm85vtNzVbHB29+DB/+qlj1n14NCitYOec4sdxTeisgJmWHiRVVo5pd9Qd2QFgVAaFRcY3z+IVP/Uthy/BT7QCtXoSnT3UQxklqKsciuNHkOzNWyfVdhpGM6DyLJAKdRhdtF5pXh6DndPfzEe49vAjGgO+7YhYABqadRoZklA0InZwsI7GfsuYhDJjI9e/QG8BlPYTBq8mVrmjouGdKpAfrHgIdr+o5I2UZ0WRODGG8Jp5/X2XtWJJRnDKEqmIIZtpq6iEMKRmNwhACMyB4sn/SwErlTEAQ/Zo+d9+xwl5c+iHjHIZA92pC3rutMpbPiYBgU9UoTvCVR0/gh68+Hy+7dAeu3j2Jy3eOGQyh6jsqv/7imSYmal5h6qm+itI1e3oYqA0GIYy5kb1h+whffewEvvzIswDE6q+oQGulF6EXJVjUVotBnOqxjZxVv75hDyC01sm6h7oVEL791CK+XFDgRPdhfqWHKOEmQ9A8hMm6j26YbVCXbkDUL8toEENIB0iRsRzrHoJLASH9XL+AQOY4XYtozNZHMkp0hpDdUUFtAAAgAElEQVQNUHmG76pscDjdHM5U1j+7YDAE8d/kIQxTTEbPHH13XkCYG6+OlGVkS0a0ynUdqmjPplrSM0sMgZ4RPctoUIEpkI7FYdOKddB39qIECedwaB/nPFM5p5JZzzLaf6KFn//UvxR2M9YXp7oJHVgBYUzeu63yEc6JgOBqrRUA4PhSF0GU4Lt2TYIxhs/+h1fh/7zhqkJT+YLJGnZO5OvhgJlzrTcro4FBkhEhjJPCIAKIB3OhFci9Gcy6AR1kcp9eDdNmWVFqhjWrbraXUWTKNUurISYlQ9BX7KtBjP0nVnInQZJonlkWjEmZyjLtlFZ5VC1tG3zDFab1p9+tXqT078KAoLeukOxPD562rPCntz6J//jpfwEgJki6jwDgO6zvyps8oYrr4GgeQ+ilq0/9GgBgvOqh6jkD6xD0iePkSjFDGEbmoQlmpk9AmB2rorcOyWhu3AwIadqp5SHEXBnOaUDIMgTHWq3fe/gUfvAPvq6eW875ukzlbpiyuCiWDMHNz2zSGYLdlyiME1Xfoxv3OvS4pktGdG9oDJWS0SbAZdRlUdzsp06JFdyeHXXxd7lyyXgIVfFjnD9Zw87xanH3Tm0F3NJWrhQoJnMCgj4x2JN2NxT9dvSeLHmrR1qpxwlXAzyME/hemrFh6/S2ZLS0GmKi5gsDWluxt4MIYcwz220C6WR0XHaApcApurhygyHQd5jXR7UZ60s7Ha96mB2rDA4IbDiGcNsT87jnkOhFFMSp9AVAZhkNZggX7qjj6OnVjGGd19yOgkSz6qHmuwM9hMhgCOmzeNrKMspjCCvd0JBRaIKhDCfbQ2BM/G00U9mSjKriOfAKCtPCOEGFTGXfNJX1LCPP6nb66DMreOzZFSWf6fd2PaZySAyBSYYwwEMgBhFpkhGd/10HT+UmLwwylfUsI6CUjDYUKVUVN/5pGRAunG4Y77OzjObGq9g1WUPNd2VAyPcQdIagT+7dHMmIMSkZGQEhmxraDmI8u5x+Xy5D0M6HfARBv8X1Nnw3U2yWMo4EScKx3E0ZAk1Qoo21eN8jx5cz30sMgYxNum+04qfVuGII1sM8DEOgCaoo7XRZ7tO7e6pe6CHoeyrTimupj4dwfKmrvje0GEK/DeKBdCBfOttEL0qMjp1A+hvrkzVNyiIgOENIRrqHoElG8jpW+tQh/Orf3493/12a5EfBg/wLXdajRcLIHoK8jvGah4rn5DAE85hRkmQYgt36Io8h0H/Tuev3diRTOaDUYNNUHpRlRH9PNIZAz/b8Sg8HchZVSU5Aoc8CaeuK8VIy2nhQQKACk6cXO3Adhgsma8b76pZk9POvvRyf+blXAkhz6vOifS9nkAPpwJjWGMJEzUcUJ8bgtidtmhgOnEy7gudlxOTtQxvEacZGo5pO8gRjQgoidII4YyrrrOKxZ7K7mtleSsUT+i5du5KMakUMYYg6hChlCHn3vCU3Xdkz3ciVaIB0oHmGqZyeiz5pcM5x7PSquj+BxrQA9N3+kc4TAObGxTO1tGoFBHmt+mqQgsQYMYQBprI+kdKquOo56v7mBR3CsaWuwXCJsewokIwm6z4qI5vKiTq36/edh1dcNgMg6+Wp6zLSTi0PQfsN7dU6/b70POnnujzCBKpnGUUyIHgF+0DHCTe6/Ip/p+ehj6E82ciQjLTnSmUZWZLRKNczCs6JgKAeRE4MYRW7p+pqVUKo+WZAGK/52CNZxPmTdXTDxJhQCLRCGat6uabyZD1lCFMNsTevPgBtyYg+R62kgXyGoE/MpI2HcVph2ai46IRmnYMeWE5IBjJZ95W8lCTc6Ho6VEBwHXguU5+zJSN7taZWdHFSKMPoATOPsq+ogFDHkdP5tQg6Q6B7shrGGJNSoF6cttgR9R66mW14CK4DzovbL9PnSJPXjx3Gifq9DWYon5VGxUXNG0YySr+bFgC7puqK9fTzENq9yGCytOIsCggTddG/aZRup2lAcPHhn3gx3viSPQBSySjjIWiFacQuKTiaWUZO7sqcrrcfQ/jO06fx9o/d1XcRYpvKrsPgOMV7KtM507OXbqWZMrCK6+DOA9n9wxPOlQdmFKZZvYzKLKNNgN1U66lTHVwo/QMdRpaRZ96aK84T9QqPPpOVUGgA7GhWctNOdVN5qk4MwZwYHjy6hPmVnswoEudpMoQ8yaingp2SjLT9CRoVL1PnEESJehCPytbEUw1f+QDdKFZMwXcZHh0iIPieA89xlKFO964wIITpZFu0B4O+aspboa30QoxVPVw800QQJarvvo48DwEAJmoexqqewVxoExLFECLTQyiSO+xzpAl2xWgrnl6jPhnS/RobVjLS7gkxhF1TtbQwrU9zu04vMgIFBY+ZptD5ezkMwR/Qv6kI9KxWrDFE9zDPQ1ABIZN2msB1RJ8j15qcKVhQoKPrcx2WkSnvOXQKtz1xMrOntQ497TSKOVxGLbfzGYJqbWFlGQEpK37ppdO5e2QkPPUJ9Gc9tCqVq54D32WlZLSRoLmAfrgji52MfwCICZDkJZ0tAMBVcgPwR49nJ0h6IGfGKqZklOMhTDYqylSWX4VWL8LbPnYX/uTW/cYq0WQI+ZLRZXNNAGnqoT64aJLXV0W9KFYTNWnvs2NV9d6OttXnvl2TOHp6NTO4TrZ6OH8ilduIIVAwTD0EWomnn48T0Y+HgmTRik1f7eWteFty4/a9MlA/cSK76Z6eduq5jrrf9YqLiVp+QIgSjiThCOJ0BQgAvlrd5k/adI5pQDANekKed9SseqhapnKeTKYHRuoIesFkXZnKK/0YQhAbWUxtmaVFz0JgyS2TdR+VAdXZRdAlIx2ek592Shk9AMAYMzKuSLoB5EZXOfn/NkOYaVYyWUb0nH3zyfysH0BsnwuI34gYQlHtQ2xM6CZDAFLfbNdkPdcr45yjSp+3PAT6XrofY1WvZAgbCZ0hdIIIJ1sBLtyRDQiMMdRkRKYfhDA3XsXsWAWPPrOMThDhE3ceVoOFJv6ZZhXdMJVBdHONVuVTdV+ayhzNqgeHibTJ050QS6tmVfLBhf6S0YmVLi6ZaaLiOjjVln2SknQio0prfUIKogRTKiCISXBmrKLY0WqQMoQXXSgqtPdbk+18q6faeQCizN5zHOWFkGRU9VzUfMfQP2nQUpAsMpYNySivBkNKRpfPiYCw/9mcgKC1PRDnSem4HibqvhHojmt7ZgdxgiCKrSwjOZnF+TUhWYYQodWL8Jv/9KBKDrC77tJvWvdd6SHIHPpOiKt/6xbcvt/cKtPIMlIBoYblrkg7Lsoy4pyjbTGElW6EMZnuan9G9xBGkYzoM1XfnF7oubQlI9uv0TOuYi1Y2HUIylTW0kUBYGZM7GtiNOyT77njyeLtR43WFcpDyO+DFicpyyXpyshykpla4zU/9x7qzfPsLCN61gjjNV/9tpuNcyQgiH9HCcfTp8QkmBcQALF6JGPLxlXnT+DRZ1bwqW89jd/47IO4UTad62krEyD1BOj1mi86p1ZcB82qq+oQqp6DZsVTmTyrQWxUJesPUh5DOLHSw86JKqYafuohaNkx9VyGkKhVIZmxM82qSrFtS6MZEOm2AIyHsReJLQqJMQFAxXVzTWVArECXjPRZcex0A5/8B12fdIs8hLGah+lmBbNjVTxxIsvcFEOQ0biiMafJum8yBE1y6sn0WTvLCBBMZN/7vpiRDkOLISx3Q9x96BT+6o7D+KLckYwWA/r3AOL5qGmr4hMrXbR6ER4+Zn6HPqHTTnVTjQo4F5lGakKzAlYgTVKzXkak7ToOg++yjGQ0ISWjdTEE1xxHKrkjhyH42gKs5jtGlpGnGIIZUG2GQOc6O5Yn24nn7IGjS4VFa3pgiRNRmFa0KU+cpM85PZ+JxmaWu5EY817+TnsJR25A0AtLCVvZ8fQcCQgpQ0hTTrMeAiAGZ9XPDwjPv2Acjz2zgpseEE3xPn77QXDOFUPYIR/EVkBdJ1NNve67aFRdeI4jNUrxwzeqrjJuO3L7Th20WrBfp4l5bqyGHc2KmXbqkYdgpoPGidijeFKuzkkymm74Knh0glgNnrwunLQyvXimoViF74occZV2qgWEiZo58ep+S951EfSJyF5hdcMYQZwow23vzrFcyShJhHHnKIYgzrdZ8TBZ9w1v49hpjSHI5nF5DGH/iRbCmONxi5HQ+U7WfbgOw0o3VEGa9hyYbPjG5EATdMVzjFUxTewnrR5NtpfSrLqK7VFwb1TczAqcPAy9yIw2/gFEoKR73JUra/IQSEJbC9RzbzGE4sI0cxI0GEKSFq3ZbexThkBMnQKC8EV0ZkrPZsKBbx3M9xHs5naU2ZRXmJYkWVM5SrgaE0uroVwECtnNlgATzYOwexlVyoCwudDT3WjrxIuKGILvGj2NdFx1/gR6UYJ7Dy/istkmHjq2jG8dPKVW7ylDED8evV71HNQrrtyK0ZGyg3igmtW0I+lqTkDYKdMYe1GCrz56Au/863vAOVd56DsnqphuVLQsI91DMCUjeze1I4urmG748FxHdSzVJSO6Hp21qA3Rx6oqhz2TdqoF1ElLmkkZQn/JKOjDEFSFr0zJu3znGPY/28oMuijhih0AaaBqVLMB4bjWRps8Hp260z2lXkgL1mRNk5zvOmoAU0bag0eX1b3QB38vipU8qZvK9G+7aZ8tXdQrrmJaaXCvqP2o7fsVROnE1OpFqi2CLg1R8CbJCEDfdNs8qEBnTWzFhWncyPgTGVcpQ3B1hpBknwsKBHaml/77dsIYu6fqqPkO3vsP9+OGP/x6pq7IrEMQPdAKm9vlSD5xwpX3uLwaol5xC7PT9CylMLYlIzsg+BuyT/QwOCcCgsPSgHB0cRV131UrVBv1ipsxlAm6bv7BN70QUw0ff3PnYUU1d1i7rPWiWFZYCobQrLrwPSY0ajnhkM4PiBUKDQQaBCTbdMMYdx5YwJcefha9KDH2odUZgm6G2qYyDXpaVZ5Y6anWyxQ8OkGsZBy1itcmbVq1zo1X1Wd914HnOrkMwZZmUoaQv+czQe+hY8sWtFqi9NG9541hpRfhWasbbSz70RDovJoVFxPWeR1f6qqFQ8oQ0ueAJiwKvJnVO1XUukwFhNNq7wXxPXmSEfkteh0CrVTtbC57Zd3wPS0giIBGv5l+z/R7TIGWPASgT0BwTX/h4Mk2PnrbAQxCIFN2HcuH61eYpgffqu+oexEniSEZ5aadWoVps+PEENLfdzWIMdXw8cvXX4nL5pp49JkVI2kDsLudJto+zjlpp1odQsoQErWYXO4KhkDeiP3bJTwNmHHCtQwnrhg+oWQIGwydIayGMRoVF4yx3PfW+jCEy3eOwXUYLp5p4EUXTuHFF03j4Ml26iHICZIYQi9M1CRUr4hWGL6UjER6qGPs0tYJIhVcKIvnvAnZKz+KVRbJcjdUNQRz41VMN/108x0tf74p93Ogoqi0LiJNg6UJJJWMIo0hpN9NUAxBbogOiIlWN/wMyciaeIkhkKlclGWkTxrZgCCOR2X91MLc9hESiyFUtN9isu6jLTdjjxOOZ5a7ylfSAzaBNG5a9ZN0RituxRAcBxM1wT70zqqAYEX6tXS1VuG6TNJVkpH5eQo69Hw2qq5iWlQNS7+n7gmYtTHi9VYvUgxL17npt5qo+er66do+d98x/OfPPzJwU6demGRSToF+hWmWZKTVZOgMwTaVSUIrZgi6ZBShUXHxzu+7DP/X6/cByMqVqpdRNET764EMQXwfXVcQJ1iRvhIgGYL8/KGFNq75v2/Gt59azPUQJmpbt43mOREQ9C6LUczVSiUPL75oGi++aDr3b1XPxY+/ZA/e+b2XgTGGKTnZZU3ldOcqkk+u3j2JF+6Zgu+K3dO6chCQjguIyZGOtXtKeBwkGXXDRB13pRupwqSZMSEZne4EqisqXV+6+5S5k5ZeF0EGnM4mKIBMyVW8PnAoIMyMVZRk5Mu0U3WfbMmoT0AozjIqrkMgk5smtL07BXN7wtL1dUMS0LKMpIcACGp/YqWLOOG4eCZtBR1EiRHYbMnoZCvAcjfEte//Ev750WfTlgNeyhAW2+Ygnqz7xnX1tO+oeUIy4pxrAcFiCJI10bk3Ki4umWlirOrhG/vFnhh5DMHYtEk+A+1epNip7iFQAsBk3ddWt2Y23aBGa3qDQx0kGdkTbJhw6/lJ5bNY+w3tNvYpQzDTTosYAo0HlVFnBQTdlCdT2bOK4QhRzBWDjLUso7omGdWkhwCIe/iZe47grR+5E70oBudQaacH5tsIY+Fv2i1TAMGEW72o74ZOGwVv8FvOflBASDg3NuPIw3t/6Kq+x/qdN16j/ntCBQQhDZHJ2eqlK3IaGB/4X64GALXPwWoQCcmoaktG4rO7p+vAITFxVmQGCk2EK91IPeyTdR/TjQoSLh5CfbXVtExlCggTGkMgFpCmqApTuVFxla+gZzgdW+piuuGj6rmKEVU8Rxn3gG0qe1jpRUjkAKNzmG72zzIK+jCEZUsymh2rYKLm4cl5MyDQdxJUllHVNRrvUWHXxZIhUMWyvlKjCUt5CO0eDp1sY2k1xKGTHeUNeI6D8ZqPp0914LkMjKUbI03UfSRST3blvaDgSZ12e1GinoFT7UC9F0i1/Imaj2eXe2hUPLgOw0sunsbXHhcBgQJtUSW8Ygjd1EOoeq6a7PtJRuQNrHQjnJcmmWVgB1N1D4s2yLGqwmu+q9iRzRAAMppTP4GuiZ6ZWflMGx5CEGPXlLjHeoq1Dl22ocVEEUNIeJqFFseiLxjn6bGDWGwcRYuQIBIMIZKFp3raKQX+bigYq82uxmseEi6uQZ8vNgPnBENQBTHU1rYPQ1gLJutiv9NOICZ+kmio42kvZ2CoCt0gFgxBTgTnT9RElpEcmLumBDOYavhy9ZhKRivdEMurERwmJn1aFZ7qBIaHULcCAg3scVn/AKTNzdIU1QgdKat5rpCC9JXUgfkWLpO5/7Rnbs13jbRBWzLiPNX9bVO5SDIyi3XMAUn0mQIwYwznT9YyK2raBpGgMwS67oV2oLq37paZZ9Rqwm5dAaSdRRdagcrs6UWJquj1NQ9hsRPiCslear6jpB614tYlI7na7IWJtp8wN2QnuifEjIjVvezSHeo95M30Ywicc7QCkXZK94Um1TxTOYjNiXcwQ8iXjByHwWHZ3cY4h7FIq/mukk5FHYKjPg9kC8HswrSphp+pVu4EsXrGaxVxvCxDMNO8qdtpXpaVqFSmljgpS9Azq+qaZKS3L4mT/ICwGsQFaafUvmLzfYRzIiDQgxRzblDQ9YJWmfMrPVR9YVTWfAf75UpVeAimQU26bCcQhU9k5l55/jh6UdolcbfcyWqq4Ut92ZSMaOtLxpja5GSxHSBK0lbCFdeB6zC1CqeBU/UdtZIhmasiC/JShpDSa10yOnCyjctmRXX0G1+8B//tJ16EHc2KEWT1yUBJM1qQBERQ8l1W2LrCrENIcHihjcOyUG/FkozEdVSVrk+IE+QGhEbFVczo5EpPrUYvmBQBgdp4+zmSEUl1C60ejp6mgBAjioUJyZhgisvdEKc7AV6wewIVz8GY9I/E9aTat+4hAMKv0bO69CBH92RCSUbi+l9uBISqugZCx+qv1QmEZNHMMZXpd6I6BP17A41d9INgxvmJGXbXWF1qI9ACCChmCPq/7cK0iudgouYZHgJ5h0C6is+kcmv/3w1juA6GYgiJltWlt7+p+15uQEgSbpjKKiCEidHoj5B2PN18H+GcCAj6g5SX1jUqJrVsHdFzxMHrnn8evvDAMwhjsVNYNhdb/H+7F0kPQTxAlMFEK9B9uybgOQyXzjZVBgqtzFrdSFWT6uexZElGjDFMN3ycXBGTGD3wVc9VqyWSfQChPy+0eugE6eCp+mkK4HI3xPxKD8+TJu5kw8e/vkbsOayv8Iy+QdaeCDQIqVivsHWFJRn9xmcfxK//jwfE9VOXUD0gjFWMXcQAkaFimMrKbPfUfr8n24FKISVWJlb8dvtrKjiSnUWDWElUtsQ0ITdGP9UOMNOs4JKZBhoVLzVpSYLRJk59HwD9ntBvB6SBhJgR/UZX75lUwY4665obMGlZRlGSuX9Vy1Qer3rGLnOBdr4A0Or1n5iEFJY/xuy8fhUQdMnRd1RQjJPUE3O1hZ1+P1KGQD24nEwygzCVxfXWiiSjTEBwZJZRNu021uoQIlnfox8bAOoVJzWVI661dBe1HbTgoIXMapjPEF5z1U7c9quvwSVyIbaZOCc8BJ1qRsnGSUZkzs7LgAAAP/rCXfj8/cdx+/6TfSWj1UDkoP/w1ReAMYY5KWHQCvTK88Zx/2/9ABoV0fisFyZqZbbcDcVeyHJiINlJZM2YK9uLZ5o4fEqsrFUnRVkIBaQMAUhbfIdxui2myI8XA+XAvDjO86RkpIPuadVzjAwue5Mcvc+N2LazqFLZlIyWVkM1Ma10Q9R8xxg4s2PVrGRUwBDqFRc7GhUwJhjC6U6AiZqnVsx0TkZhmpywdF/v/iOi4KwXit43dA/Ga0Im60UJphoVvOTiaRxZXFWLgVDTvskH0XcK01euBkOw6kjod696Ll504RTuP7KkvAjTQzCzjOy0XcNUllXKADKmsp6y2g9FHgIgAoL92wIwVsVFWUZ2G/tEMYTEOJZgCGnufpKI/T3SQkrBhvNMZd9lykPoxxAirbAs4Vydk84QGhVPyUoGQ+DcYBh0fOUh5JjKY5vsHRCGWiozxm5gjD3GGNvPGHtvwXvexBh7mDH2EGPsk9rrv8sYe1D+82bt9b+Vx3yQMfZxxpifd9yNADGEhLJw+pjKawENzGeXu2ql9+or5zBe83DjfcfQDeNMTQNNGp1QeAjPv2AC777+CpUBQZpxVZOTbIYgJKNINY9TbSdkvxp9orx4poHDC6JoqacXyvlZhjA3VsV8q4d2kFax6pLRk7IamBrqGdflpAEh7x4tWwyh6rtil7bCOoS0K2sYi0mSjiFy6M3HZaZZwUo3MrrCJn08BM91MN2o4GRLSEaz41U1EOk+Gx6Clz0OVZj3InMg61LWjmYF73/Dd+Hj73ip+jtNXLqkqDOEbhir+2kEhMT0EOpaDcu/fcXF+LEX7c58B2CayjpDGM8pTFvWmCdN0rQ9KD0/o3oIgGDIccJxz6FT+Nx9x1IzPqdSmVsSr2d5CHZhWrofMcNE3VPPC038eop3zXezASGIjZTsfvs4m5XGXBnc9p4qxR5CthvsqkyD3igFYxQM/GbGmAvgwwB+CMA+AG9ljO2z3rMXwK8BeBXn/AUAflG+/noALwZwLYCXA/gVxhjlJ/wtgKsAXA2gDuBnNuKC8qDvmKZ3Vlwv6OHpBLEa0FXPxQ0vOB+3PPQsTraCzARJP3acmL1yaIJebAeoeE6moKrVjdSDv9KNTIYgJ2+SmyraauuSmSaOL3WVYQWIB1FJRjkMYVWTjPT8+CfnW/AcllvlTUHWbvthS0YGQ+gjGYVxojKfoljUj9DKVMhl5oqJUg1PtU2JxbXuI5BODLNjFSy0Apxs9TDbrKoBSkxMX7XqiwjKRtInJD1ZQc/imm74ckWapuYakpFvegidIMZqGGNmrIKK6xjFaeRT0PmT3AgIZvpf/s3VRlYLwfYQVJfVSioZ9QyGkL4OZBnCujwEWW38F7cfwu/d/Fim/7+4FyI1O12pp91OAW2HMm4FhCg9lmAIkbz+bECwvbEkEW3iaUwBVKlcsEGOVmkcc55rKtt1CNQ6RJnKllKxGsbGfiZnAsN888sA7OecH+CcBwA+DeAN1nveCeDDnPNFAOCcn5Cv7wPwdc55xDlvA7gfwA3yPTdxCQDfArBn/ZeTD1djCJGmSa4X+mpCHwA/9apL0Q4iPHWqk2MqZ41KIH1YT3VC1KwHoua7hj5O++PakhFVxtoMARB7QKS9lUQ1tusw4xrmxqs42Qqw0tX1VketpA7Mt3HRTCN3BeO6/RkCpXamPoYoyrNXaYQgTlTQEgwhwUovQpxwwz8hUGDTjeUk4dBjP/0WTZWuWpUMoYfZ8YqalGjC1CuV9eBga7m2h6AzhCmt9bnd/16XFGnf4XYvUvLG3HjVaF9BcicFj7qfnXRtIxgwV/SBLhkVtK5IGUJ+2ukghtBPMvKlqdwOInTDON3VTpeM1DaasWQI4lh6k0pAL0xLK4x9Vxj7ev0LLTp0RlWvmIsRyu4b154r1xEBLG8LzURrbREnSYGpbKad6gyBc8GK9PpY6tC6rRkCgN0Antb+/4h8TccVAK5gjN3OGLuTMXaDfP0+ADcwxhqMsVkArwFwof5BKRW9HcAXR7mAYeBZHsJG3XB9JaivDPbtmsBbXiouM8sQtOpXTYaoa5O6LTNVPVelRgLEECJMSg/Dc0VKIzEEnX5fKievQwvtdAUlV+c7mhWDicyNVREnHM8udy2GID735Hwr1z8A0kpe+3qbFRcXzzTwD/ceERO7nCwYY5iopxXWNsI41Xz1TctbvSg/IEjpS5dY4qRIMkrlMpKMZnSGICUWs7ldliEQemFstB0fr+kMIRsQSILpapIRTc6tXoRVKTXOjlWMamUKOhSsG5WsrkxBrWcwhJTx9XTJSAYhu1LZDgh2YdrKOiQjagVBfbvSlh+6qZz6KbkMwU471RiC2mlM66HVCdOd6Qh1SzKi4DChBXPXceC6xZXK5DHESRqcjIBQcQ0JTwUE6SE4DIZa0ZUsvuJtzIJ1FGxUKPIA7AXwagBvBfDnjLEpzvktAG4C8E0AnwJwBwB7SfgnECzitrwDM8Z+ljF2D2Psnvn5+ZFOTu9ltJGSUc131QRoT4S//ANXYrzmGStEwJxYdBmCHqRT7SCToVHzHWMQnmoHWA1j4+Edq3rKf9Cp6MU7REA4vNA25Jrd03U8z/ICaD/gKOFqFU2SURQnOLzQyfUPgHSw2oyIMYb/9Pp9eOJEC395+yH0NF9l91RdpW7a0I3tKMOQXYUAACAASURBVEmN1uXVMDcgUMW1zhDEZJLNfmpoBW3PLHextBpidkwPCGIiMSQj7b/nxqsqqDhMMoQobXWhMwR9P+10D149y4gyn8TxVnpi5VyvuILBrJhpp3q7k0Y1yxBoMjGyjHqRCky9KNE25nHlfXH7MwS7DmGQZBT2MZVl98/VUKTXUnDUn9l017TY6mUk/q7qECzJSHT6TTO9umGi0myBrL6v1x1QcJi0GILLsh4CeRuu48i/pwyhZjGE4rRTLusc0vu0Gp4FHgKAozBX9XvkazqOALiRcx5yzg8CeBwiQIBz/gHO+bWc8+sBMPk3AABj7DcBzAF4d9GXc84/wjm/jnN+3dzc3DDXlIHeZVG0dti4G06ZRvZEODtWxc2/+H34pev3mudSkK9Pg3ylG2X2Y9AfMs9hahLVGUqj4ilZRn+gJhs+phs+Dp7sGAzhN39kHz7+jpca3zM3nhrMFKBqvotelOD4UhdBnODSmfyAQJNdXrrhv9p3Hl571U784Zcfx6lOqAb8nuk6llbD3E6Ooaz0BCj7gqv7s7QaZgJtHkNI5CqOcNGOBnaOV9W1zY5VFfsxJaNskz49LXKs5qnv2zPdEHUIWgW8rkPnSUahNsHS/TIloxg1TwQEQzKSixma2Bo5klFF7kGgb6PZDiKjx5GddkqSUS8SzRVpUrQ3z1F1CAUMQWw+1RvoIRBDCKJ0K1m7ME2cq5CUbIZAaaAZhqCn/tbTYi5a/TesyXpV81boOdDHlEvdTq3KaooPLqP22NDSTm0PQcsyijWGkIjsR30+WA3jTF+nrcYw33w3gL2MsUsZYxUAbwFwo/Wez0KwA0hp6AoABxhjLmNsRr5+DYBrANwi//9nAPwggLdyzrOJvhsI0rdjJRltHCWzB4+OXVN1Qz4ArMwVNysZAdntO/WH7LyJGp6Rje30iadZ9VQfGvuBunimmWEIVc/NSA56QKDVY913sBrEyhSeLugS6xV4CIR/+/KL0A5ifPvwYsoQZGUwVfzqCKJEnZ++Ij3dER7HhMUQmhXB1nSvJbaa2/34dXtw+3tfqyYYYhWAKGyjbTaJjRl+j3ZdY1VR6ew5DBfuqKsNdeg9xBDGql7BvswcnHOptadZRg4T19oNhX8y3axgqRMaDfR818GLLpzCv3r+ebjqgmz/CD+HIXR6sfrdyEOouI76bpKM9Cpl/fpVVtQAhvA7X3gUP/LH38BqGBdnGTnCQ6BVO7EVY8c0xRAS1aZCfJbGMeS/TQ8hiNJEDRoby6uh+o6G7SFYdQf6tQMiAOVlGam9uh0xqSecD2QIuocQxfmS0SqljW/ngMA5jwC8C8DNAB4B8BnO+UOMsfczxn5Uvu1mAAuMsYcBfBXAezjnCwB8ALfJ1z8C4G3yeADwZwDOA3AHY+w7jLH3beiVaaBJIeYcUZwYMsJ6oQJCQSGODc8ICFnJCDADAACDMeyaqqmHb6KuS0aukox8azBeIlNPaUDbec6EnVpAsFNe86qDjesqkIwIV++ZBAAcPb2qMQShxecFhChJJSN9oxNiR7ZkxBjL1CLYHgJjLFO7QJgbTxv1paayLu+lxxmveZgbq+KCqRrqvodeKE1l+Z6a3B1PbyIIQNOTk8y+w7R3buohOJisiw119A3nfZdh50QNH/3J6zL3wP4OQjuIsEOeC2UZ6UV9dA5UBDdhpZ2GlmSky5dPn+qo3+Rkq4fjS13pjRRLRrEuAVJGl3Z/05qMWEkzQCr9kuSWl3ZKvxmNjeVupCb+uu0haKYyvUdfZLkOcj0EMplpR7VIk4yEPwb1fWpPCc1DSLgmGVGbGelp6NdwJjBUtQPn/CYIL0B/7X3af3MI2efd1nu6EJlGecfcsqI4I+00MbfrWy9ShpA/EdrI23QFMFcvWYaQ/r9or7BofDcgGEJe2ikgsmL+6b5jWOmGqFiFYzqaVQ+NimuYkOQhLFv9g2wU1SEQdo7XcMFkDceXuoaHAKSbu+gItSwjvRDq6cX8gADIauWWxRD6/NZ6Dcas1qivnccQtP8eq/r4lR+8EovtAH9952EpbXjGe8ZrnmEoA+liIIp57kb04zUfLZKMfFeb1MRmK8PInb4l8wBCApvSmt61epGRskrnQPIUBYR0MjMrgfVK5V/+7/dhvOrhY+94qbFFZ/+005QhtPIYgpKMEqNjbVHrCrpWvVOozhDou/Rr1pMlAM1U1hZZottpNstIMQSWbrFJr3mOSDEOoiTby8hIOxWLALqmC6ZqiuHb43crceZC0RYirXAc3P56rZhYI0PQV+d27jXBHkz6pHGB3DAHyEpGaWGOeS6XzjbBOfDo8RXVcrcIJBsZDCFMVApfEUNQaacFmwsBogW4OKY4h9mxCqqeozZ30UGDW+zElk5AR+QWqLkBoVnBQltjCHIVVgRDMpIBoeo5aWGadt9d2ZgNEFLQFeeN4+WXzaDqiVbNQWymM4uEAvMcVaGXbGsi7kV6v5pVV0pGMiDUzBqOcIiECNVuIk7wzr++B3/8lSfQDsTeB7R3sl3YR9dJNQ+2ZETBK08yOrq4mqkxAYrHg+eIyZJW5NQI0jMYgm4qax6Cmx8QdIZAkhmNy+VuGhAavi4ZOWaWUY5kJLqdOhmGoPbqdpgylYm16C0/itJO6XgOS2XEXZN1dR+3tWT0XEDaAwWyDmEDTeU6bRIzHEMwJSNTzkiNXDvLKD32zgktIGgP75jGMOwH6srzRZ+kB48tDQxcc2oHNfNcKP2xiCH4SjIqPv41Ujaie8UYw57p/EyjQGryvutYDKE4IMyOVTN1CP2C/6zq1pp2na24Tm6lMpD+duOG3OLKlaxpBr79lZfgx68zMqyN1aJeNU5QklEQo+67mSrvKB5sOOr9h7518BQ+fffTqpEdtaho9ULV6VT/zLPSmyJmo8tPYu/w1FTmnMutXHsqh78Xxdi7cwwTNU+xPxuey4wNe1o5bExv4xHpWUZsAEOI8xhCpMzjfpJRN0cyom6nnMPoeEptKmgDHZ0huJpRXNdM5UCTCYlxOYypcXP+ZE0FijMZEM6JXkYp1ZTm36ZIRsP9iGYdgvkZKtQqMpWbFdeYCPWHV09BtB+oy2bH4DkMK90I4xrDyAMxBKLX5F/Q6nGsiCEMkIwA4Oo9U8b1AMDu6UY+Q5CD23OZERCO9JWMREDgnIMxIU3U+zCEmu9iTO6vTDJaxXOUdGJrub7DEABGXxlR5RtnulT+b99zaeb7VGGaLhkZDMHDcjdCNxI1GPoqF0gL0/qBZI5elGC5G6pVZ7Piouq70kOIjQQCus5jMjATc6JjiT2muTpOW27kRJOcvsn9nuk6/v9f+J6+3U7133Olmw0IZtrpEN1OtS000yyjVG7rBKIViP57kmZPz0oqGdkMIZWbK1ZzvTQgJGoy95zUp6r7rprwRdqp7M8UpwyBjq8zf3te2EqcWwwhwSaYymaZ/yAU6dJAuoKxGQJNGmM1T61OfZcZ79MnKTuLquI5qqBskGFlS0Z0TidWukbWRPa6KCAMloz09+yZrhd6CJ6k33rbX8qwymcIFbFVoVx12r2M8jA7VlFtL8R1pO2Z7WulgapvUlL1RduHcAjmqfZs1iQj00Pw1M53ZCoDumQ0XEqi7zo41QqMRnyNis4QIuMa6Jk4vtRVrbr1Y1FKKpBKa61epGokFEOQhXb9ngFamBDSjK4cUzmKDQ/BKQgICRfjWjdk674Lz2HKQ9DZAWBuSAToprJemMa0uSO9mYmSfGTaKTcZgi4Z6UE17XYq/s2kqdywFnqlh7DJSH/UZOPTTqkOoY92rkMf0LYkoSSjgjqEsWoaEPRVLWBOUnmTxhVSNhokbRVJRidWeobhZkMVpvWRpHY0K3j+BRPGamjPdB2LnTCzTy8VGdkMgSY5W5+n4wNpcZqddpqHV10+i1deNqP+Xw+YdvD0HAdVzzFer3qu0oeLsrfs40UxV4amLRnRJCs8BLnKlX39RUri4GdXZzmEZtVTwUt4CKbsBQiGMNusGM8Vdf8kWYbucasbKRkxZQjZdu82REBIA3yegU/PfzdMEMdplpHd3C7WIh61D6HfgCrhl7uh0ZuLYO+alleHQJO5+M7UH9EZgmO1rvBcBt9lkt066tr0e0jFeK48/lTdN1SBUjLaZLgsfZA2sv01kK5U7f5DRSjKMgLSSThTqSyPPVb1lExka/mDAsJV54/jc/cNZghX75nETLOS6utycJ5c6WVqKvKuaxBT+vufe6VxfqQ1Hz29iivOE0GLc67ysT3HwWInNTuXVkOjfbcOYjXUzG1QlhGQbm1KMNtVmJ+l3dB00PWSLNEP+haSel8pQrPqqb0LarpkpHkI3hBtkH3XwYmVrrqeIErQrLqKIbR7kXEddM1HT6/iEqvwkGoUaCVNclKrFykZkfT3fumm6h64ZvtrCvb2nsp0XH0BZ++HoK/aezIo688WbZLDkW3zoe+rPI2UIej3xStgCHqWEdUppAxB+F46I/FdIUnRIVIPQVz3VKNipJ1v9+Z2Zz0cR+xtSz/cRrW/BoBJMpVHYAj2hKMkoyKGUEv7oo9bkslYVX+gshPTlecRQ+h/7a++cifu/U/Xp60rlGTUK8wwAvS00/73oWkVa1Etwm989kH83d1PAdD62rvMaLx23oQIUnlyEZBOJHoTsUEBwYa+yq+62caEdl96up+tbjRQ+9X3F0g9BE0y0o5N8lyj4hpZRsMwhKqXdkn9vr2zAMSEWPUdtGU31WYlayqvdCO1taj+Nz1DhhjCSjdSNR9pFlJxhTLBHnutAg+ByZYgRpaRyhak9tfpqp12udOfLdokZzWIMo0A1ZaxKpgJI1///R0tLTQ3IDgMjmxtEelBwnUyE7zOgOm8Hcawe6qOy3eOWQGkDAibDjLagOxEvB5cPjeGa/ZMYl9O1Wge+noIWrsIHXmS0YQ1OTf7ZBkBaabRWlcfFJxavagwwwgAXPmdaz3+C3ZN4N+8aDcOzLfxO194FIC2i5ZMOyXslL2WCgOCawUEzo3mfcPAYAhWYPVcljHV6bcJtMK0wmNrlb95WUY6y6Pj6hu96O0x+sF3mZLN/t0rL8GPvHAXrjp/HFXPxaKs5B7LYQgA1Nai6lieYwQw8hBWuqERECgLyfa/bNgsSnkI2nUxxoRZH8ZmlpElGYmOo5DnkO0USveukyMZ2bumLXVCjNc8WbyYfp+rtb0h6AHBVQxBTztlxvfpmWsAEErJiDGGP3zztfjQm15oBpAyIGw+HMYUtd1QU7nh48Z3fQ8u35nfBdSGK9kKkCcZpS2ndaQN0Dwl22RaNwyQjPZM1zFW9YY2vwn6ufRjCEXdTgcf38Xvv/lavPElu5VcEmmmrm7UUiV1UUCgia2nMYS1NjIsqhMBxKRVxBDofPtB6dEFkpE+SdflRvCijXMkP8eHykCpeGnu/FXnj+OP3/oilXZK+0WMW5lSBNpaVL8mXeKiNuOtXmRUhfeiRLbzHsAQrMWYaiRoBd+qJ7KAEg7NQxD/pkKxKEnbmwRRkqnypU1y8kxle1/lk61eWpwof0cx4UN+l2YqW5XKIp1d9xBMSdP3rICQ6JKRk31/6SFsPjyHqVXZRjKEUUCZG0WSkS0/0cMyLuWWques2UNgjOFHr92FPdP5+eFF0AdSPw9hmLTTfmhWPARRorJFADGQ9IyLuQEBgSYjQzIaYCrboAmFBquOPdN1ow4EMH+rQVlGtBjIa10BmJliiiHUPSUZDcNCAPP3t1u0U+FeXusKAJi1GIKQjFJDdFbLMtI37+kEIkV0sIdg/l15CNYireank2i6p7L4mzKVE+G7tXqR8hD054U2yfEcR8mNBFsyEntiiPf4ngMEcR+GIP6dVionBmuYyDGJDclIpZ2m52p7DmcK50xAcBymVjkb1f56VJAua6/2iiUjaSrLQfyeH7wSL7po2niP4SEUTEy/bRmow0D3M/plGdEkNKyXYoModke2AAaEh5BmajDVfmEQQ6CAkiSjS0Z5QfX/e/tLMm0/9AlwULogk4VIYZLWIei/dW5AqPkq1XbYKnu6hppvrjwrrqOyafLSTgFkPATfMpXp7yvdCPNaESAFrWGyjHTk7U4nzt1Vk2jxfggJxurUo0lmGWnXsnuqjpOtHjq9CPt2mZKunWV0shXgeZLlK4bAWCazSXxv2twulYzSOoT/58e+yyhk823JSDOV7fMByjqELYHnMDUYNrJSeaRzkQ+/PXGrVE/PXi2J12kQ/8z3XoaXXGwGBIMhbOAGG/qE0tdDWC9DoA3ue3Guh1DzXSPlNg+pZCQGecxHl4zyvBDPdTImtX69wzxXvssQRkm6c1xBLQlNEJRZBQxfZU/BjKro1bkWBB8zINgMQZwvMYTxmg/fZaoOgW4HZUKt1VSmOgM70NY8V7UhtyuV9T2VaRe0PA/hba+4GOMycytTh6BlGXHOMd/qqZRrOoZZh5Aa2AlPM4pcy1Qmo/hCbROlisuMfa0paUK/5tJD2GK4jqMmiu0gGen/JqSFaebDO9Xw8by5Jl6wa7LwmIMko1GhP6h9PYQh006LQMGwHURpj3zX0Va7aRrmQIagSUajMoRhB6U+AQ5z3z3XKZSMck1lbSvIMOZDnRcds6jbKmD+lvrrM83+HkLVczBe83FyRWw9KpotagxhiLRT9b192NjMWAXHlkTldLaXUcoA1U5wIdUYpceablbw868V+5E0bQ9B22tjpRchiBKjwSF9r91yG0gDkutQ91aNIeTMLVnJKDWg7fPRv/9M4BwKCGnxyUaayqOAdOCMh1AgGVU9F1/55Vfj+68o3iBIzzLaSElMX8H2ZwhSMhqyp5MNVUPQi41dtOha6lqhVrGHkA0Io3oIww5K/f4Ms9DwXVMyKpqk61pAWOlFYj/wOBnqt7U3ick71yLJaNZmCJZkVPEcfPfzZnDTA8fRixJVR0KZUMNKRr7LFEvJm0QvnmniqYWO8Rl7ctZbpOc1JASAf/fdF+P7r5jDdZfsMF7XJSMqBiRDvZLDEIzCNI0NZNJOc34f33WM/k25klGZdrq18LYTQyhYGdnVwWuB64hWFrTJ+EZB7+/ejyFQfrqtQQ8LWsF1NIagZxnVfEeZ2mvJMhq1DmHYQbmWLCPxnlQyor2lCc0cyWii5oFzkZ4ZxnwoyYiuYapezBCKJKMdOQwhiExG8xMvu0hlhFGSwtLQklEa4Kt92NjFMw1tkiWj32IInKv7VNSQsOq5+Kuffhl+8AXnG6+nhWmJqrimYEiSq6v1JdJbZSdapbJoXcER5+z8RvA9B5qlgCBHMqp5a1tYbBbOmYDgOOlEsZGFaaOAHjJ7NVPXWk6PgrGqt+GrC8aYMpb7ZRm9+KIpfOWXv19VG68VtM9xJzA9BL0vzOVzY3jhhVO49qKp3GPYG8yPFBDWyhC0CXAYw9d3RUqoSNE0v8NoJ+Gbq/zl1RBhks1My/0OeVw7cBb5FXTfJut+5rorGYnLxSufN4NLZoRGvjsTEIbLMqpXXPWc5zIETYO3GYLuISiG0M1nCEWg81wNY5U+O2t7CIzhqgvE8/ztw4vqs3qlsuMwRDEHFV/nPW92kIoUQ0jf62nPeukhbAE8x1E5xxvZumK0cyHabN7+7907i594+UWZ9gHDorkJAQFIGUu/LCPGmGqgNwp0DyEwGIL0JnwXkw0f//QfXlX4PRnJaIjmdjY2myF4LlPN7eyMrKpHDC89Lk3qpzshOB9uMaMYguUhUOFeo+JmdpKreE4uu6t4Zh8e2mDpJ15+EQCoZ5VqJQYtZuh7G5W0Jibvvl0008h8Rt8PIUk4OE8XEnnbnvaD44h2892cgKBLRhdM1rF35xi+/sQ8umGM377pETyz1FV/dxltoUmLzZyAYCV56Psh6KBxVtYhbAFcJy1MO9OSUWqmmedx3kRtpNRQQrPiGdkMGwUxyMO+DGG9UGmnvRjjNekheExNgMOwJsZEU7E07RR9N8jJw2Z7CBXXEYVpOX1/GGNoVsWWnCQnkG9D9QPDZJDRBJRlCGa2mvE318nUIAAiMK2GcaY760+96lI8/4IJVRsyLEOge1TTJKO8CfBibVGk6hC0/RCon1HDH40hAHJf5SDGvMyWIrlMN5UB4PuumMPf3HkYH7/9ID7y9QP40RfuUn+nLTYHeQg6whyGQOezPEQLlM3EOcMQXMa2jalcxBDWi7Gqtymtc0lv7echrBdkireDSFFq33XU5FYfdkc6z1EFiHrbg2GRZhkN9zlDMhriuaLmbnmSESB+Q91gpEmdWlH4a2AIk41sXyLArFJWfytgCNPNCk53AnVPdQb1vXvn1PUrU3nItNNGJQ0Ieb/RWNVTjfQyvYy0Te1TU5l2Gxv+96Y9EU62etjRrKjj0zU6WkAIogR/8KXHAQCnZfBziCEk3Ng0x0ZRQLDXKjTOzqRkdE4xBGUqn+HCtKK00/WiWXU3ZXVR9V0wZu7KttGgDX5sD2EtDAEQK9QgFrnlCcea006L/J1+32d/dtDxwziBw/Inz7GqZxQ1kUxHDGFYnwIo9hDyNjl69ZU7cd0l05nXdzQqCGOOhXYPFdfJ3E+SOZaHLUyj3cT8VLYqum8X7WjgZCvI9DKK4zQgkO9GpvJa0p5rvmA/pzuJkV1F50Pf+/JLdxhNFk93RHBW3U652dzOhn19eZXK4nxc+f6yUnnT4WqtK850YVrFc4zdkjYKF+5obJJkJHr4rHVyXQsqsuirE0QqC8OXfV4AZLpVFh5HDlyaU0dNOx3NQxgy7TSmKuV8hhBoSe9kKi/IHkRDZRlRHUJBlpHdjwkAPvSmF+Yea1rKKM8s9XKDpGIIw5rKWoCnSym6b5fMNPHtp04rRm+3sQfEtboOy915bRDqFRfdIMZCOzB2kKNFFU3YNd/Fa66cw7PLPTzx7AoWKSA4cj8EGaAclr8AsdlmkFOHQOez0VmCa8U5FRACVfB05k3lzTCOfv2Hn2/0XNko1LUN3zcLjIkOke1ejFDL0dc152FQ8RzVNhlY+2/dLxWy6Lyr8juHTTvthQmSJL8R3HSzYrRJGKt4cB2m+gatpZdREUPI8xCKQIVqx5dWcyd7CmprTTttVNzCJo8EMpb1HdMcJtI+iUV5jrj/IwUETTK6dDb1LHRTmfBHb30ROAde88FbcbojJSONIYiq+PzvLmYI2fM5k4YyMKSHwBi7gTH2GGNsP2PsvQXveRNj7GHG2EOMsU9qr/8uY+xB+c+btdcvZYzdJY/5d4yx0RLYh4SuUw6jw24m9HTKjUTNd9c02IfFeM3DdHNzAwIgfISOUamcblg+dECQefN68dBaoDTyNUgPSgtfA0Mo2l3sff96Hz7449eo/3cchrmxKo7KvaSHmTAUQ8hUKqdNEodFyhC6uQEh9RCGk2x0yWjQfbtYBgR9YnYd08R1HJEhdVTuB92sDp+yXfNdnJAV17Oaf0K+lT5nVD1X7mLn/8/2zj5Yzrq645/vPnv33twklxASkpAEQiARA01DGlBBXkSBgC9BsTSMtfgKVpkpg1KhOPhSO1O01tYpU4ojU+posVrFOINA28HSKigReQ2CMYiERhIi8k5yb+7pH8/v2X325u69+3b3efbu+czs3N3ffXb3PC/7nN8553fOqWrqU0jVMqp1WxlrWVViCPsnpuZeIUiKgGuAs4BVwPmSVo3ZZgVwBXCimR0NXBLG3wysBdYArwE+JimpMnU18EUzOxJ4Bnh/W/aoBmlTLmsLoS8qZLqSoFE+vv4orj539eQbtshgf9zAPR1DSH4g9Sbr9fcFhVBOHmpMhuTH29/AG/sbCAYWCwX2ThBUXjp3kCMPrs7lWDDUz/agEOq5dg+Y0UexoPLNvCxnsXYMoRaJhbD7xb3juozi5C3VXdwuubmn8xBq3QTXLD2QA2b0VdUFigohiDvGQtj1/B6WHTTI7y8ZP0dlPNYfs5CtO1/gleHxYwjjuX/SS6/LFsKoxYUH67AQigUxXGOyMlDqAoUAHA9sNbNtZrYXuBHYMGabDwLXmNkzAGa2M4yvAu4wsxEzexG4H1ivWDWeBnwrbHcDcE5ruzIxaW2fdbXTvkiZBo4aZfn8WRPWUWoXg2EZ4N5yx7S0QqjfQtjTgoXQTMC/MtOtZ/YuRvaN8srw5N3FEg4eGihXPK1HrnPXLuGmj5y4n5svuaE3YkWmlUotefuLUfl4T6YUyzGhOhTC4fNmct8nz6hy5xQLcWJfWeGHHAqAD596ZEPxwXe95jA+8eZXA4wpRld79VN66XXSMW0kNMipFRNML2WOCkolplVvt2hooCqWkQX1XBmLgSdSr7cTz/bTrASQ9EMgAj5lZrcA9wGflPQFYBB4A7AFOAj4nZmNpD5zcbM7UQ9RlYWQrRY+ZvEB5cxPp8JgqciLe0Z4OdR96e+rVDttNKicnkE2QqN5CFBRCPUo+WKhkvlb74qYhUMDDe3PjFLEMYv3V+BlC6EBhTCzFJVzO2odk7h3QSzbZL+tcgyhL0pZgvWfo4JCHkJqmeeMvojFc2ZwzrGN30I+cNJyzjx6IYfMqfQJGRtUTpPuVJj0QxhNerXXODflwo9RgX1mNfMQPnrGq/jIcPsXhTRCuxzORWAFcCqwBLhD0u+Z2W2SjgN+BOwC7gQa2mNJFwIXAhx66KFNCxjlyEL4wEnL+cBJmYqQS2aWIp5+YS+7Qv/mgZRPtW6XUTHidy8PVzUsaYTmFMLEM900cQwhuIzq3Kd0c5dWXI3JPjWSTyKJuTNL/Oa58WMIUNn/eqy4cgyhFFEYCb76BiZoxahQlZgWFcQn3ryK2QPFhs5ZmrR1AOMHlRPSBQMLhfheMhJcWLWutbLVWSxA6P0M4+QhlKL9ynR3mnqO4JPA0tTrJWEszXZgk5kNm9ljwKPECgIz+yszW2NmpwMK/9sNzJFUnOAzCe+/zszWmdm6+fNrV/ucjKqgcsYWgjM+nIMOJQAAFFJJREFUg/1FXtw7ws7n97AgdCZrapXR8L6qNoeN0GjpCqj4zesubrcvKW5Xv8uo/P4WFkQk39eIhQAVt1EtBZaM12PxJH72GaWJi9vVIu2igfimffLK+fs1jGqFRLGM7zJKVxWO8zJGQ6JcbQuhsp+FgsqVUxt1Z3aCes7E3cCKsCqoBGwENo3Z5iZi6wBJ84hdSNskRZIOCuOrgdXAbWZmwO3AO8P7LwC+2+K+TEh1Ian8nQgndiO8tGcfTz33Srl/crHRGEIo1zzSpMuovwWXUb2lqZ9+YQ8v7t1X3sfJWJBSCK1cu8vmDXLKyvn7NVeajLlhhVmtG3eiaOpTCBUXYLm4XQPnKA7ijpZLYE+FtZ9MQsYNKg/UthBqTT7STZeKBTE80pz12gkmnSqY2Yiki4FbieMD15vZQ5I+A2w2s03hf2dI2kLsErrMzHZLGgD+Jyyveg7441Tc4OPAjZI+C/wM+Eq7dy5N+oeUtcvIGZ+Z/fGy053P72FduGk1mpjWnySmtRhUbqQESHJDrEeJFCMxanDcsgO54IRldX3+wrSF0IJCGCwVueF9xzf8vrmhxlHtoHKwEOpyGRWCLFE5ebARl1HcsrLSn2AqkiUPmTOD2f3Fqh4jCWmXURT6IZjB8AQWQtoN+fKwapauyAN12Y5mdjNw85ixq1LPDbg0PNLbvEK80mi8z9xGvIKpI6TrF2UdVHbGZ7AULzvdMzJadpM06jJKksQ6G0Oo30JYs3QO6w47kOveva7ufUrHELIo3T435DNMFFSG+iyEZL3/ogNm8Ovfxg1wGlG+Y5vaT8Xk7uxjFnHqqw4e15+fdhmlO6rtHdk3aQyhFMUtN4dz7DLqnUzl1LF3CyGfzOwvlpN8yi6jcqmD+m6E5cQ0a04hJDfpRjq/JTPjegK+G9YsZsOaxlbDJH0KxvYM7hQVC6F1l9Hy+bP4yV+8kYOHBtj5fFKfqYGgckHss1TXsin4LRcKqhlnqXYZqfz9e0ZGJ8hDCEXzystOm7NeO0HPTJXTFoIHlfPJYGpGllgIr1k+l43HLa278U5/X1S17LRRhTB3ZonPv3M1b1m9qO73JN2upioDXlLZbZRF/ko5hjCphdBYkLyZ+v+dsBAmIu0yKlZZCLXzEPpSVmdU0LgtNPNCz9wZk2tOU1BUzmkPVQohWAjzZvXz1+eubjAxbV9VV6tG+cN1SzloVv0JQpVVRlN3XSVuoyzcneVVRhMkpsHkWcpjaaa6ZzLDbuX8tkLaZVSQym6lZ18erp2YFlVcarFC2L+FZl7oIYVQv5/XyYbBVBCv3hU4YymF/rVJqeKprNCakNwQp3Kikcyqs7h+506y7LSRGEKaiRrk1CJKLfNMXneSoTGZyksPjHMYHt/9Ul0xhIKomamcB3pGIVT6svbMLncd6cJk6bX3jZC4NZImJvWuTmqFRQcMsHBoYEpnfAtmJy6jLGIIoZPYZMtOGzzWE/VUrsXY4nadXkI+e0ymcpLU9sKekUkzleNlp4WyhZDHGELvBJUTheA5CLklsRAGS1HDyVMJyawzKRc9tgT0VHDBCcs477ilk2/YAofMiRVCo7PwdjB3cGoshIFkuW7DMYRKpnKnb6oDfVE5wF8oiCUHzkACs9rWSnrlWiFVhj+PruueUwgeUM4vSQyhWXcRVH58SeP0oQ4ohHRV1qninX+whAVDA/tVMO0EB83q5+3HLuZ1yw8a9/+VVUYNWgilxl1GSXXRpJZRFhb/0EBfuUnOQF/EwqEBdjz7Ss3JZim97DQlbg4NhN5TCB5DyC+JhdCsuwgqP76ng4UwNIV9oDvJnMESbw3N3TtNVBBf/KM1Nf/frIUwf1Y/V5x1FGcevbDu95RLV5RLkzT0lW1haEax3EMa4lpIO559paa1km7Lml7tmEeXUc9Ml10h5J8khtCKhZD4sXd10ELodZpdZSSJi045goUH1D8BKEZj+yF0/hY2e6CvanXToXOrO7uNpWrZaWoTVwgZUg4qu8sot5QthNmtWwi7nt/DYA4ajvQCjeYhtEKU9EPIaJURxFZn+nsThRDVmZiWkMe5ac/8WhJt7EHl/DKrvxh3vVrafDOe/lQMYar7QDsxjWQqt0qU9EPIUiHM6Ku6mU9mIZTzEKJClVWQxzyE6eFgrYPkZGXdT9mpTVQQP7jsDS19RnqVUdbdp3qFRspft0pUiPshNFvNth3MHSxVLbFNlp5GNSab6RhCekLqFkKGFHzZaU+QrDJ65qXhjiw5dZrPQ2iGqBBbCKNTWMtoMj506hH8w/nHll9PZiEMliIuOmU5b1q1oMpCyGMMoecsBA8qT2/S9XbcZdQZOmkhxD2Vm+930Q4Wz5nB4lTLzXmzSgyWopplNCRxxVlx7+a0i8vzEDIk8qByT1ClENxC6AgDDbTQbJW4dAWZJaaNhyQ+fOoRHLVwaNJto6oYwlRK1Ry9pxByqJWd9pFe6TJdchDyTmIhNJJx3CxRaEG5L2T75uX3fPFpK+rarnqVUT5kT9Mz0+VyUNkthGlN2kLwGEJnOPqQId574rKamcztJCqI0VHKLqNagdy8kneF0DNTKA8q9wbpWaq7jDpDfzHik289uiPfFSm2EEYtm/LXrVLwPIR84NVOe4N0tqwHlacfUaSqZad5DMxORNrFlcc8hJ65O3o/hN6g2kLoGQO4Z0iK2412qUKI5BZCLkjuE+4ymt64y2h6kxS3G8moY1qrFHK+7LSHFELjpXad7qNQUFkpuMto+pG2EKRsEtNaIcp56YqeuTsmJ8JdRtOfZKWRrzKafkRRxULoxt9yNB1KV0haL+kRSVslXV5jm/MkbZH0kKSvp8Y/F8YelvQlBbUo6XxJD0i6X9Itkua1Z5fGxxPTeodEIbiFMP2Y0Rexd2SUvSOjuXS5TEaU89IVk94dJUXANcBZwCrgfEmrxmyzArgCONHMjgYuCeMnACcCq4FjgOOAUyQVgb8H3mBmq4H7gYvbtVPj4aUreof+YgGpuv+tMz1IWqs+98pw18UPIP95CPVMl48HtprZNjPbC9wIbBizzQeBa8zsGQAz2xnGDRgASkA/0Ac8BSg8ZgaLYQj4vxb3ZUK8p3LvUCoWmNVf7Dr/sjM5MxOF8PJId1oIVctOMxSkBvUohMXAE6nX28NYmpXASkk/lHSXpPUAZnYncDuwIzxuNbOHzWwY+FPgAWJFsAr4ynhfLulCSZslbd61a1cDu1aN91TuHUpRwd1F05REITz78nDXK4RutRDqoQisAE4Fzge+LGmOpCOBVwNLiJXIaZJOktRHrBCOBQ4hdhldMd4Hm9l1ZrbOzNbNnz+/aQG9llHvUCoWPKA8TZkV2qzGCqH7JneFnOch1ONkfRJYmnq9JIyl2Q78OMz8H5P0KBUFcZeZvQAg6fvA64BXAMzsl2H834Bxg9XtwoPKvcPsgaJbgtOUmaVKDKEbJ3fpyzKPFk49v5q7gRWSDpdUAjYCm8ZscxPxzZ+wWmglsA34NSGIHKyCU4CHiRXKKknJlP/0MD5leFC5d/jsOcfw6bd1praO01m632VUueXmMQ9hUgvBzEYkXQzcCkTA9Wb2kKTPAJvNbFP43xmStgD7gMvMbLekbwGnEccKDLjFzL4HIOnTwB2ShoHHgfe0f/cqeHG73uHIg2dnLYIzRSSrjF7YM8KBg6WMpWmcZGVUXnVZXevyzOxm4OYxY1elnhtwaXikt9kHXFTjM68Frm1Q3qbxnsqO0/0kFoJZPl0uk5G4jPIYUIYeylROToBbCI7TvczsrzRA6kaFkHgqXCFkTKIIPKjsON3LjL6o7G7pxsS0xFORV9F75u7oQWXH6X4klVcadaWFILcQckHBi9s5zrQgiSN0o/s3KuQ7qNwzCuGQOTM48+gFrFs2N2tRHMdpgSSOkNdZ9kQkE9K8llXpmepfA30R//TudVmL4ThOiyRLT7vR2vegsuM4ThtJXEZ5nWVPRN7zEFwhOI7TVcx0C2HKcIXgOE5XkbiMunGVUWXZaT5ld4XgOE5XMViKg8rdqBB8lZHjOE4b6eqgsuchOI7jtI9yUDmnN9WJKLqF4DiO0z66OTGtkPM8BFcIjuN0FbO6ODEtcpeR4zhO++jmZaceVHYcx2kjM8vLTrvv9hV5HoLjOE77qOQhZCxIE0Re/tpxHKd9VMpfd9/ty5edOo7jtJGk2mk3WwiuEBzHcdpAJajcfbcvdxk5juO0kVldnJiWKIS8lt1wheA4TlfRXyzQF4m+LkxMmxZ5CJLWS3pE0lZJl9fY5jxJWyQ9JOnrqfHPhbGHJX1JocyfpJKk6yQ9Kunnks5tzy45jjOdkcTV567mvOOWZi1Kw+Q9D2HSjmmSIuAa4HRgO3C3pE1mtiW1zQrgCuBEM3tG0sFh/ATgRGB12PR/gVOAHwBXAjvNbKWkAuC9LR3HqYt3rF2StQhNEeW8/HU9LTSPB7aa2TYASTcCG4AtqW0+CFxjZs8AmNnOMG7AAFACBPQBT4X/vQ84Kmw/Cjzd0p44juPknGRlVF4thHpcRouBJ1Kvt4exNCuBlZJ+KOkuSesBzOxO4HZgR3jcamYPS5oT3veXku6R9E1JC1raE8dxnJzTK3kIRWAFcCpwPvBlSXMkHQm8GlhCrEROk3RS2H4J8CMzWwvcCfzNeB8s6UJJmyVt3rVrV5vEdRzH6TzJUtluVghPAunozZIwlmY7sMnMhs3sMeBRYgXxduAuM3vBzF4Avg+8DtgNvAR8O7z/m8Da8b7czK4zs3Vmtm7+/Pl17pbjOE7+SFIncqoP6lIIdwMrJB0uqQRsBDaN2eYmYusASfOIXUjbgF8Dp0gqSuojDig/bGYGfC95D/BGqmMSjuM404685yFMGlQ2sxFJFwO3AhFwvZk9JOkzwGYz2xT+d4akLcA+4DIz2y3pW8BpwAPEAeZbzOx74aM/DnxV0t8Bu4D3tnvnHMdx8kTeS1fUs8oIM7sZuHnM2FWp5wZcGh7pbfYBF9X4zMeBkxuU13Ecp2tJEtNyqg88U9lxHKdT5N1CcIXgOI7TIQo5z1R2heA4jtMhim4hOI7jOFBRBHktXeEKwXEcp0PkvbidKwTHcZwOkawyymsegisEx3GcDlEoCMljCI7jOA6xlZBTfeAKwXEcp5MUCnILwXEcx4mXnuY0hOAKwXEcp5NEcgvBcRzHIQksu0JwHMfpeSJ3GTmO4zgQK4S85iHUVf7acRzHaQ8fPX0lKxbMylqMcXGF4DiO00E2Hn9o1iLUxF1GjuM4DuAKwXEcxwm4QnAcx3EAVwiO4zhOwBWC4ziOA7hCcBzHcQKuEBzHcRzAFYLjOI4TkJllLUPdSNoFPN7k2+cBT7dRnHbisjVPnuVz2Zojz7JBvuWrJdthZjZ/sjd3lUJoBUmbzWxd1nKMh8vWPHmWz2VrjjzLBvmWr1XZ3GXkOI7jAK4QHMdxnEAvKYTrshZgAly25smzfC5bc+RZNsi3fC3J1jMxBMdxHGdieslCcBzHcSagJxSCpPWSHpG0VdLlGcuyVNLtkrZIekjSn4XxT0l6UtK94XF2RvL9StIDQYbNYWyupP+Q9Ivw98AM5HpV6tjcK+k5SZdkedwkXS9pp6QHU2PjHivFfClcg/dLWpuBbJ+X9PPw/d+RNCeML5P0cuoYXpuBbDXPo6QrwnF7RNKZGcj2jZRcv5J0bxjv9HGrde9o3zVnZtP6AUTAL4HlQAm4D1iVoTyLgLXh+WzgUWAV8CngYzk4Xr8C5o0Z+xxweXh+OXB1Ds7pb4DDsjxuwMnAWuDByY4VcDbwfUDAa4EfZyDbGUAxPL86Jduy9HYZHbdxz2P4bdwH9AOHh99y1EnZxvz/C8BVGR23WveOtl1zvWAhHA9sNbNtZrYXuBHYkJUwZrbDzO4Jz58HHgYWZyVPnWwAbgjPbwDOyVAWgDcCvzSzZpMU24KZ3QH8dsxwrWO1AfgXi7kLmCNpUSdlM7PbzGwkvLwLWDJV3z8RNY5bLTYAN5rZHjN7DNhK/JvuuGySBJwH/OtUff9ETHDvaNs11wsKYTHwROr1dnJyA5a0DDgW+HEYujiYdtdn4ZYJGHCbpJ9KujCMLTCzHeH5b4AF2YhWZiPVP8o8HLeEWscqb9fh+4hnjwmHS/qZpP+WdFJGMo13HvN03E4CnjKzX6TGMjluY+4dbbvmekEh5BJJs4B/By4xs+eAfwSOANYAO4hN0yx4vZmtBc4CPiLp5PQ/LbZFM1uaJqkEvA34ZhjKy3Hbj6yPVS0kXQmMAF8LQzuAQ83sWOBS4OuShjosVm7PY4rzqZ6IZHLcxrl3lGn1musFhfAksDT1ekkYywxJfcQn9Gtm9m0AM3vKzPaZ2SjwZabQLJ4IM3sy/N0JfCfI8VRiaoa/O7OQLXAWcI+ZPQX5OW4pah2rXFyHkt4DvAV4V7h5ENwxu8PznxL76Vd2Uq4JzmNejlsReAfwjWQsi+M23r2DNl5zvaAQ7gZWSDo8zC43ApuyEib4Ib8CPGxmf5saT/v23g48OPa9HZBtpqTZyXPiIOSDxMfrgrDZBcB3Oy1biqpZWh6O2xhqHatNwJ+ElR+vBZ5NmfkdQdJ64M+Bt5nZS6nx+ZKi8Hw5sALY1mHZap3HTcBGSf2SDg+y/aSTsgXeBPzczLYnA50+brXuHbTzmutUhDzLB3G0/VFiDX5lxrK8ntikux+4NzzOBr4KPBDGNwGLMpBtOfGKjvuAh5JjBRwE/BfwC+A/gbkZHbuZwG7ggNRYZseNWDHtAIaJ/bPvr3WsiFd6XBOuwQeAdRnItpXYp5xcd9eGbc8N5/te4B7grRnIVvM8AleG4/YIcFanZQvj/wx8aMy2nT5ute4dbbvmPFPZcRzHAXrDZeQ4juPUgSsEx3EcB3CF4DiO4wRcITiO4ziAKwTHcRwn4ArBcRzHAVwhOI7jOAFXCI7jOA4A/w8MA+4LIPW52wAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import matplotlib.ticker as ticker\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"plt.plot(all_losses)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 234,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x1305afb00>]\"\n      ]\n     },\n     \"execution_count\": 234,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import matplotlib.ticker as ticker\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"plt.plot(all_losses)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 235,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(3m 30s) logloss=14.94 \\t accuracy=0.57\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 看看测试集合的正确率多高, 发现效果很差，说明一点。。（数据不均匀）\\n\",\n    \"\\n\",\n    \"out_t = cnn(Variable(te_x))\\n\",\n    \"\\n\",\n    \"# softmax 用来计算输出分类的概率，然后max是选出最大的一组：(概率值，分类值)\\n\",\n    \"prediction_t = torch.max(F.softmax(out_t, dim=1), 1)[1]\\n\",\n    \"pred_t_y = prediction_t.data.numpy().squeeze()\\n\",\n    \"target_t_y = Variable(te_y).data.numpy()\\n\",\n    \"logloss_t = log_loss(target_t_y, pred_t_y, eps=1e-15)\\n\",\n    \"accuracy_t = sum(pred_t_y == target_t_y)/len(target_t_y)  # 预测中有多少和真实值一样\\n\",\n    \"print('(%s) logloss=%.2f \\\\t accuracy=%.2f' % (timeSince(start), logloss_t, accuracy_t))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 236,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(3m 49s) logloss=14.25 \\t accuracy=0.59\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 又回头看看训练集合的正确率多高，严重说明：数据在采样的时候不均匀，倒是数据有丢失没学习到\\n\",\n    \"\\n\",\n    \"out_t = cnn(Variable(tr_x[:10000]))\\n\",\n    \"\\n\",\n    \"# softmax 用来计算输出分类的概率，然后max是选出最大的一组：(概率值，分类值)\\n\",\n    \"prediction_t = torch.max(F.softmax(out_t, dim=1), 1)[1]\\n\",\n    \"pred_t_y = prediction_t.data.numpy().squeeze()\\n\",\n    \"target_t_y = Variable(tr_y[:10000]).data.numpy()\\n\",\n    \"logloss_t = log_loss(target_t_y, pred_t_y, eps=1e-15)\\n\",\n    \"accuracy_t = sum(pred_t_y == target_t_y)/len(target_t_y)  # 预测中有多少和真实值一样\\n\",\n    \"print('(%s) logloss=%.2f \\\\t accuracy=%.2f' % (timeSince(start), logloss_t, accuracy_t))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* * * \\n\",\n    \"\\n\",\n    \"其他的信息\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 加载训练好的词向量\\n\",\n    \"\\n\",\n    \"from gensim.models.word2vec import Word2Vec\\n\",\n    \"\\n\",\n    \"model = Word2Vec.load_word2vec_format(\\\"vector.txt\\\", binary=False)  # C text format\\n\",\n    \"# model = Word2Vec.load_word2vec_format(\\\"vector.bin\\\", binary=True)  # C\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 加载 google 的词向量，查看单词之间关系\\n\",\n    \"\\n\",\n    \"from gensim.models.word2vec import Word2Vec \\n\",\n    \"model = Word2Vec.load_word2vec_format(\\\"GoogleNews-vectors-negative300.bin\\\", binary=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 测试预测效果\\n\",\n    \"\\n\",\n    \"print(model.most_similar(positive=[\\\"woman\\\", \\\"king\\\"], negative=[\\\"man\\\"], topn=5))\\n\",\n    \"print(model.most_similar(positive=[\\\"biggest\\\", \\\"small\\\"], negative=[\\\"big\\\"], topn=5))\\n\",\n    \"print(model.most_similar(positive=[\\\"ate\\\", \\\"speak\\\"], negative=[\\\"eat\\\"], topn=5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"with open(\\\"food_words.txt\\\", \\\"r\\\") as infile:\\n\",\n    \"    food_words = infile.readlines()\\n\",\n    \"    \\n\",\n    \"with open(\\\"sports_words.txt\\\", \\\"r\\\") as infile:\\n\",\n    \"    food_words = infile.readlines()\\n\",\n    \"    \\n\",\n    \"with open(\\\"weather_words.txt\\\", \\\"r\\\") as infile:\\n\",\n    \"    food_words = infile.readlines()\\n\",\n    \"    \\n\",\n    \"def getWordVecs(words):\\n\",\n    \"    vec = []\\n\",\n    \"    for word in words:\\n\",\n    \"        word = word.replace(\\\"\\\\n\\\", \\\"\\\")\\n\",\n    \"        try:\\n\",\n    \"            vecs.append(model[word].reshape((1, 300)))\\n\",\n    \"        except KeyError:\\n\",\n    \"            continue\\n\",\n    \"    \\n\",\n    \"    # numpy提供了numpy.concatenate((a1,a2,...), axis=0)函数。能够一次完成多个数组的拼接\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    >>> a=np.array([1,2,3])\\n\",\n    \"    >>> b=np.array([11,22,33])\\n\",\n    \"    >>> c=np.array([44,55,66])\\n\",\n    \"    >>> np.concatenate((a,b,c),axis=0)  # 默认情况下，axis=0可以不写\\n\",\n    \"    array([ 1,  2,  3, 11, 22, 33, 44, 55, 66]) #对于一维数组拼接，axis的值不影响最后的结果\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    vecs = np.concatenate(vecs)\\n\",\n    \"    return np.array(vecs, dtype=\\\"float\\\")\\n\",\n    \"\\n\",\n    \"food_vecs = getWordVecs(food_words)\\n\",\n    \"sports_vecs = getWordVecs(sports_words)\\n\",\n    \"weather_vecs = getWordVecs(weather_words)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 利用 TSNE 和 matplotlib 对分类结果进行可视化处理\\n\",\n    \"\\n\",\n    \"from sklearn.manifold import TSEN\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"ts = TSEN(2)\\n\",\n    \"reduced_vecs = ts.fit_transform(np.concatenate((food_vecs, sports_vecs, weather_vecs)))\\n\",\n    \"\\n\",\n    \"for i in range(len(reduced_vecs)):\\n\",\n    \"    if i < len(food_vecs):\\n\",\n    \"        color = \\\"b\\\"\\n\",\n    \"    elif i >= len(food_vecs) and i <(len(food_vecs)+len(sports_vecs)):\\n\",\n    \"        color = \\\"r\\\"\\n\",\n    \"    else:\\n\",\n    \"        color = \\\"g\\\"\\n\",\n    \"    \\n\",\n    \"    plt.plot(reduced_vecs[i, 0], reduced_vecs[i, 1], marker=\\\"0\\\", color=color, marksize=8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 首先，我们导入数据并构建 Word2Vec 模型：\\n\",\n    \"\\n\",\n    \"from sklearn.cross_validation import train_ _test_ _split\\n\",\n    \"from gensim.models.word2vec import Word2Vec\\n\",\n    \"\\n\",\n    \"with open('twitter.data/pos_ tweets.txt', 'r') as infile:\\n\",\n    \"    pos_tweets= infile.readlines()\\n\",\n    \"\\n\",\n    \"with open(' twitter_ data/neg_ tweets.txt', 'r') as infile:\\n\",\n    \"    neg_ _tweets = infile.readlines()\\n\",\n    \"\\n\",\n    \"# use 1for positive sentiment,0 for negative\\n\",\n    \"Y= np.concatenate((np.ones( len (pos_tweets )) ，np.zeros(len(neg_tweets))))\\n\",\n    \"\\n\",\n    \"x_train,x_test,y_train,y_test = train_test_split(np.concatenate((pos_tweets, neg_tweets)), y, test_size=0.2)\\n\",\n    \"# Do some very minor text preprocessing\\n\",\n    \"\\n\",\n    \"def cleanText(corpus):\\n\",\n    \"    corpus= [z.lower( ).replace(' \\\\n' , '').split() for z in corpus]\\n\",\n    \"    return corpus\\n\",\n    \"\\n\",\n    \"x_ train= cleanText(x_ train)\\n\",\n    \"x_ test= cleanText (x_ _test)\\n\",\n    \"\\n\",\n    \"n _dim= 300\\n\",\n    \"#Initialize model and build vocab\\n\",\n    \"imdb_w2v= Word2Vec(size=n dim, min_count=10)\\n\",\n    \"imdb_w2v.build_vocab(x_ _train)\\n\",\n    \"#Train the model over train_ _reviews (this may take several minutes)\\n\",\n    \"imdb_w2v.train( x_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 接下来，为了利用下面的函数获得推文中所有词向量的平均值，我们必须构建作为输入文本的词向量。\\n\",\n    \"\\n\",\n    \"def buildWordVector(text, size):\\n\",\n    \"    vec = np.zeros(size).reshape((1，size))\\n\",\n    \"    count= 0.\\n\",\n    \"\\n\",\n    \"    for word in text :\\n\",\n    \"        try:\\n\",\n    \"            vec += imdb_w2v[word].reshape( (1，size) )\\n\",\n    \"            count += 1.\\n\",\n    \"        except KeyError:\\n\",\n    \"            continue\\n\",\n    \"    if count != 0:\\n\",\n    \"        vec 1'= count\\n\",\n    \"    return vec\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 调整数据集的量纲是数据标准化处理的一部分，我们通常将数据集转化成服从均值为零的高斯分布，这说明数值大于均值表示乐观，反之则表示悲观。为了使模型更有效，许多机器学习模型需要预先处理数据集的量纲，特别是文本分类器这类具有许多变量的模型。\\n\",\n    \"\\n\",\n    \"from sklearn.preprocessing import scale\\n\",\n    \"\\n\",\n    \"train_vecs = np.concatenate([buildWordVector(z ，n_dim) for z in x_train])\\n\",\n    \"train_vecs= scale(train_vecs)\\n\",\n    \"\\n\",\n    \"# Train word2vec on test tweets\\n\",\n    \"imdb_w2v.train(x_test)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 最后我们需要建立测试集向量并对其标准化处理：\\n\",\n    \"\\n\",\n    \"#Build test tweet vectors then scale\\n\",\n    \"test_vecs = np.concatenate( [buildWordVector( Z，n _dim) for z in x _test ])\\n\",\n    \"test_vecs = scale(test_vecs)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"接下来我们想要通过计算测试集的预测精度和 ROC 曲线来验证分类器的有效性。 ROC 曲线衡量当模型参数调整的时候，其真阳性率和假阳性率的变化情况。在我们的案例中，我们调整的是分类器模型截断阈值的概率。一般来说，ROC 曲线下的面积（AUC）越大，该模型的表现越好。你可以在这里找到更多关于 ROC 曲线的资料\\n\",\n    \"\\n\",\n    \"（https://en.wikipedia.org/wiki/Receiver_operating_characteristic）\\n\",\n    \"\\n\",\n    \"在这个案例中我们使用罗吉斯回归的随机梯度下降法作为分类器算法。\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"#Use classification algorithm (i.e.Stochastic Logistic Regression) on training set, then assess model performance on test set\\n\",\n    \"\\n\",\n    \"from sklearn.linear model import SGDClassifier\\n\",\n    \"lr = SGDClassifier(loss='log' ，penalty='11' )\\n\",\n    \"lr.fit(train_vecs, y_train)\\n\",\n    \"print' Test Accuracy: %.2f' % r.score(test vecs, y_test )\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 随后我们利用 matplotlib 和 metric 库来构建 ROC 曲线\\n\",\n    \"\\n\",\n    \"#Crea t e ROC curve\\n\",\n    \"from sklearn.metrics import roc_curve, auc\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"pred_probas = lr.predict_proba(test_vecs)[:, 1]\\n\",\n    \"\\n\",\n    \"fpr, tpr, _ = roc_curve(y_test, pred_probas )\\n\",\n    \"roc_auc = auc(fpr, tpr)\\n\",\n    \"\\n\",\n    \"plt.plot(fpr,tpr,label='area = %.2f' % roc_ auc)\\n\",\n    \"plt.plot([0,1]，[0，1],'k--')\\n\",\n    \"plt. xlim( [0. 0 ，1. 0 ])\\n\",\n    \"plt.ylim([0.0, 1.05])\\n\",\n    \"plt.legend(loc='lower right')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/README.md",
    "content": "# 比赛分析\n\n步骤:\n\n```\n一. 数据分析\n1. 下载并加载数据\n2. 总体预览:了解每列数据的含义,数据的格式等\n3. 数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备\n\n二. 特征工程\n1.根据业务,常识,以及第二步的数据分析构造特征工程.\n2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)\n\n三. 模型选择\n1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.\n2.比较各个模型的分数,然后取效果较好的模型作为基础模型.\n\n四. 模型融合\n\n五. 修改特征和模型参数\n1.可以通过添加或者修改特征,提高模型的上限.\n2.通过修改模型的参数,是模型逼近上限\n```\n\n* * * \n\n* 比赛地址: https://www.kaggle.com/c/word2vec-nlp-tutorial\n* 参考地址: https://www.cnblogs.com/zhao441354231/p/6056914.html\n* 参考地址: https://blog.csdn.net/lijingpengchina/article/details/52250765\n\n\n# 加载包\n\n\n```python\nimport pandas as pd\nimport numpy as np\nimport re\nfrom bs4 import BeautifulSoup\n```\n\n# 读取数据\n\n\n```python\nroot_dir = \"/opt/data/kaggle/getting-started/word2vec-nlp-tutorial\"\n# 载入数据集\ntrain = pd.read_csv('%s/%s' % (root_dir, 'labeledTrainData.tsv'), header=0, delimiter=\"\\t\", quoting=3)\ntest = pd.read_csv('%s/%s' % (root_dir, 'testData.tsv'), header=0, delimiter=\"\\t\", quoting=3)\n\nprint(train.shape)\nprint(train.columns.values)\nprint(train.head(3))\nprint(test.head(3))\n```\n\n    (25000, 3)\n    ['id' 'sentiment' 'review']\n             id  sentiment                                             review\n    0  \"5814_8\"          1  \"With all this stuff going down at the moment ...\n    1  \"2381_9\"          1  \"\\\"The Classic War of the Worlds\\\" by Timothy ...\n    2  \"7759_3\"          0  \"The film starts with a manager (Nicholas Bell...\n               id                                             review\n    0  \"12311_10\"  \"Naturally in a film who's main themes are of ...\n    1    \"8348_2\"  \"This movie is a disaster within a disaster fi...\n    2    \"5828_4\"  \"All in all, this is a movie for kids. We saw ...\n\n\n\n```python\n# 去除评论中的HTML标签\nprint('\\n处理前: \\n', train['review'][0])\n\nexample1 = BeautifulSoup(train['review'][0], \"html.parser\")\n\nimport re\n# Use regular expressions to do a find-and-replace\nletters_only = re.sub('[^a-zA-Z]',  # 搜寻的pattern\n                      ' ',           # 用来替代的pattern(空格)\n                      example1.get_text())  # 待搜索的text \n\nprint(letters_only)\nlower_case = letters_only.lower()  # Convert to lower case\nwords = lower_case.split()  # Split into word\n\nprint('\\n处理后: \\n', words)\n```\n\n    \n    处理前: \n     \"With all this stuff going down at the moment with MJ i've started listening to his music, watching the odd documentary here and there, watched The Wiz and watched Moonwalker again. Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent. Moonwalker is part biography, part feature film which i remember going to see at the cinema when it was originally released. Some of it has subtle messages about MJ's feeling towards the press and also the obvious message of drugs are bad m'kay.<br /><br />Visually impressive but of course this is all about Michael Jackson so unless you remotely like MJ in anyway then you are going to hate this and find it boring. Some may call MJ an egotist for consenting to the making of this movie BUT MJ and most of his fans would say that he made it for the fans which if true is really nice of him.<br /><br />The actual feature film bit when it finally starts is only on for 20 minutes or so excluding the Smooth Criminal sequence and Joe Pesci is convincing as a psychopathic all powerful drug lord. Why he wants MJ dead so bad is beyond me. Because MJ overheard his plans? Nah, Joe Pesci's character ranted that he wanted people to know it is he who is supplying drugs etc so i dunno, maybe he just hates MJ's music.<br /><br />Lots of cool things in this like MJ turning into a car and a robot and the whole Speed Demon sequence. Also, the director must have had the patience of a saint when it came to filming the kiddy Bad sequence as usually directors hate working with one kid let alone a whole bunch of them performing a complex dance scene.<br /><br />Bottom line, this movie is for people who like MJ on one level or another (which i think is most people). If not, then stay away. It does try and give off a wholesome message and ironically MJ's bestest buddy in this movie is a girl! Michael Jackson is truly one of the most talented people ever to grace this planet but is he guilty? Well, with all the attention i've gave this subject....hmmm well i don't know because people can be different behind closed doors, i know this for a fact. He is either an extremely nice but stupid guy or one of the most sickest liars. I hope he is not the latter.\"\n     With all this stuff going down at the moment with MJ i ve started listening to his music  watching the odd documentary here and there  watched The Wiz and watched Moonwalker again  Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent  Moonwalker is part biography  part feature film which i remember going to see at the cinema when it was originally released  Some of it has subtle messages about MJ s feeling towards the press and also the obvious message of drugs are bad m kay Visually impressive but of course this is all about Michael Jackson so unless you remotely like MJ in anyway then you are going to hate this and find it boring  Some may call MJ an egotist for consenting to the making of this movie BUT MJ and most of his fans would say that he made it for the fans which if true is really nice of him The actual feature film bit when it finally starts is only on for    minutes or so excluding the Smooth Criminal sequence and Joe Pesci is convincing as a psychopathic all powerful drug lord  Why he wants MJ dead so bad is beyond me  Because MJ overheard his plans  Nah  Joe Pesci s character ranted that he wanted people to know it is he who is supplying drugs etc so i dunno  maybe he just hates MJ s music Lots of cool things in this like MJ turning into a car and a robot and the whole Speed Demon sequence  Also  the director must have had the patience of a saint when it came to filming the kiddy Bad sequence as usually directors hate working with one kid let alone a whole bunch of them performing a complex dance scene Bottom line  this movie is for people who like MJ on one level or another  which i think is most people   If not  then stay away  It does try and give off a wholesome message and ironically MJ s bestest buddy in this movie is a girl  Michael Jackson is truly one of the most talented people ever to grace this planet but is he guilty  Well  with all the attention i ve gave this subject    hmmm well i don t know because people can be different behind closed doors  i know this for a fact  He is either an extremely nice but stupid guy or one of the most sickest liars  I hope he is not the latter  \n    \n    处理后: \n     ['with', 'all', 'this', 'stuff', 'going', 'down', 'at', 'the', 'moment', 'with', 'mj', 'i', 've', 'started', 'listening', 'to', 'his', 'music', 'watching', 'the', 'odd', 'documentary', 'here', 'and', 'there', 'watched', 'the', 'wiz', 'and', 'watched', 'moonwalker', 'again', 'maybe', 'i', 'just', 'want', 'to', 'get', 'a', 'certain', 'insight', 'into', 'this', 'guy', 'who', 'i', 'thought', 'was', 'really', 'cool', 'in', 'the', 'eighties', 'just', 'to', 'maybe', 'make', 'up', 'my', 'mind', 'whether', 'he', 'is', 'guilty', 'or', 'innocent', 'moonwalker', 'is', 'part', 'biography', 'part', 'feature', 'film', 'which', 'i', 'remember', 'going', 'to', 'see', 'at', 'the', 'cinema', 'when', 'it', 'was', 'originally', 'released', 'some', 'of', 'it', 'has', 'subtle', 'messages', 'about', 'mj', 's', 'feeling', 'towards', 'the', 'press', 'and', 'also', 'the', 'obvious', 'message', 'of', 'drugs', 'are', 'bad', 'm', 'kay', 'visually', 'impressive', 'but', 'of', 'course', 'this', 'is', 'all', 'about', 'michael', 'jackson', 'so', 'unless', 'you', 'remotely', 'like', 'mj', 'in', 'anyway', 'then', 'you', 'are', 'going', 'to', 'hate', 'this', 'and', 'find', 'it', 'boring', 'some', 'may', 'call', 'mj', 'an', 'egotist', 'for', 'consenting', 'to', 'the', 'making', 'of', 'this', 'movie', 'but', 'mj', 'and', 'most', 'of', 'his', 'fans', 'would', 'say', 'that', 'he', 'made', 'it', 'for', 'the', 'fans', 'which', 'if', 'true', 'is', 'really', 'nice', 'of', 'him', 'the', 'actual', 'feature', 'film', 'bit', 'when', 'it', 'finally', 'starts', 'is', 'only', 'on', 'for', 'minutes', 'or', 'so', 'excluding', 'the', 'smooth', 'criminal', 'sequence', 'and', 'joe', 'pesci', 'is', 'convincing', 'as', 'a', 'psychopathic', 'all', 'powerful', 'drug', 'lord', 'why', 'he', 'wants', 'mj', 'dead', 'so', 'bad', 'is', 'beyond', 'me', 'because', 'mj', 'overheard', 'his', 'plans', 'nah', 'joe', 'pesci', 's', 'character', 'ranted', 'that', 'he', 'wanted', 'people', 'to', 'know', 'it', 'is', 'he', 'who', 'is', 'supplying', 'drugs', 'etc', 'so', 'i', 'dunno', 'maybe', 'he', 'just', 'hates', 'mj', 's', 'music', 'lots', 'of', 'cool', 'things', 'in', 'this', 'like', 'mj', 'turning', 'into', 'a', 'car', 'and', 'a', 'robot', 'and', 'the', 'whole', 'speed', 'demon', 'sequence', 'also', 'the', 'director', 'must', 'have', 'had', 'the', 'patience', 'of', 'a', 'saint', 'when', 'it', 'came', 'to', 'filming', 'the', 'kiddy', 'bad', 'sequence', 'as', 'usually', 'directors', 'hate', 'working', 'with', 'one', 'kid', 'let', 'alone', 'a', 'whole', 'bunch', 'of', 'them', 'performing', 'a', 'complex', 'dance', 'scene', 'bottom', 'line', 'this', 'movie', 'is', 'for', 'people', 'who', 'like', 'mj', 'on', 'one', 'level', 'or', 'another', 'which', 'i', 'think', 'is', 'most', 'people', 'if', 'not', 'then', 'stay', 'away', 'it', 'does', 'try', 'and', 'give', 'off', 'a', 'wholesome', 'message', 'and', 'ironically', 'mj', 's', 'bestest', 'buddy', 'in', 'this', 'movie', 'is', 'a', 'girl', 'michael', 'jackson', 'is', 'truly', 'one', 'of', 'the', 'most', 'talented', 'people', 'ever', 'to', 'grace', 'this', 'planet', 'but', 'is', 'he', 'guilty', 'well', 'with', 'all', 'the', 'attention', 'i', 've', 'gave', 'this', 'subject', 'hmmm', 'well', 'i', 'don', 't', 'know', 'because', 'people', 'can', 'be', 'different', 'behind', 'closed', 'doors', 'i', 'know', 'this', 'for', 'a', 'fact', 'he', 'is', 'either', 'an', 'extremely', 'nice', 'but', 'stupid', 'guy', 'or', 'one', 'of', 'the', 'most', 'sickest', 'liars', 'i', 'hope', 'he', 'is', 'not', 'the', 'latter']\n\n\n\n```python\ndef review_to_wordlist(review):\n    '''\n    把IMDB的评论转成词序列\n    参考：http://blog.csdn.net/longxinchen_ml/article/details/50629613\n    '''\n    # 去掉HTML标签，拿到内容\n    review_text = BeautifulSoup(review, \"html.parser\").get_text()\n    # 用正则表达式取出符合规范的部分\n    review_text = re.sub(\"[^a-zA-Z]\", \" \", review_text)\n    # 小写化所有的词，并转成词list\n    words = review_text.lower().split()\n    # 返回words\n    return words\n\n\n# 预处理数据\nlabel = train['sentiment']\ntrain_data = []\nfor i in range(len(train['review'])):\n    train_data.append(' '.join(review_to_wordlist(train['review'][i])))\ntest_data = []\nfor i in range(len(test['review'])):\n    test_data.append(' '.join(review_to_wordlist(test['review'][i])))\n\n# 预览数据\nprint(train_data[0], '\\n')\nprint(test_data[0])\n```\n\n    with all this stuff going down at the moment with mj i ve started listening to his music watching the odd documentary here and there watched the wiz and watched moonwalker again maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent moonwalker is part biography part feature film which i remember going to see at the cinema when it was originally released some of it has subtle messages about mj s feeling towards the press and also the obvious message of drugs are bad m kay visually impressive but of course this is all about michael jackson so unless you remotely like mj in anyway then you are going to hate this and find it boring some may call mj an egotist for consenting to the making of this movie but mj and most of his fans would say that he made it for the fans which if true is really nice of him the actual feature film bit when it finally starts is only on for minutes or so excluding the smooth criminal sequence and joe pesci is convincing as a psychopathic all powerful drug lord why he wants mj dead so bad is beyond me because mj overheard his plans nah joe pesci s character ranted that he wanted people to know it is he who is supplying drugs etc so i dunno maybe he just hates mj s music lots of cool things in this like mj turning into a car and a robot and the whole speed demon sequence also the director must have had the patience of a saint when it came to filming the kiddy bad sequence as usually directors hate working with one kid let alone a whole bunch of them performing a complex dance scene bottom line this movie is for people who like mj on one level or another which i think is most people if not then stay away it does try and give off a wholesome message and ironically mj s bestest buddy in this movie is a girl michael jackson is truly one of the most talented people ever to grace this planet but is he guilty well with all the attention i ve gave this subject hmmm well i don t know because people can be different behind closed doors i know this for a fact he is either an extremely nice but stupid guy or one of the most sickest liars i hope he is not the latter \n    \n    naturally in a film who s main themes are of mortality nostalgia and loss of innocence it is perhaps not surprising that it is rated more highly by older viewers than younger ones however there is a craftsmanship and completeness to the film which anyone can enjoy the pace is steady and constant the characters full and engaging the relationships and interactions natural showing that you do not need floods of tears to show emotion screams to show fear shouting to show dispute or violence to show anger naturally joyce s short story lends the film a ready made structure as perfect as a polished diamond but the small changes huston makes such as the inclusion of the poem fit in neatly it is truly a masterpiece of tact subtlety and overwhelming beauty\n\n\n## 特征处理\n\n直接丢给计算机这些词文本，计算机是无法计算的，因此我们需要把文本转换为向量，有几种常见的文本向量处理方法，比如： \n\n1. 单词计数 \n2. TF-IDF向量 \n3. Word2vec向量 \n\n我们先使用TF-IDF来试一下。\n\n### 2.TF-IDF向量\n\n\n```python\nfrom sklearn.feature_extraction.text import TfidfVectorizer as TFIDF\n# 参考：http://blog.csdn.net/longxinchen_ml/article/details/50629613\n\n\"\"\"\nmin_df: 最小支持度为2（词汇出现的最小次数）\nmax_features: 默认为None，可设为int，对所有关键词的term frequency进行降序排序，只取前max_features个作为关键词集\nstrip_accents: 将使用ascii或unicode编码在预处理步骤去除raw document中的重音符号\nanalyzer: 设置返回类型\ntoken_pattern: 表示token的正则表达式，需要设置analyzer == 'word'，默认的正则表达式选择2个及以上的字母或数字作为token，标点符号默认当作token分隔符，而不会被当作token\nngram_range: 词组切分的长度范围\nuse_idf: 启用逆文档频率重新加权\nuse_idf：默认为True，权值是tf*idf，如果设为False，将不使用idf，就是只使用tf，相当于CountVectorizer了。\nsmooth_idf: idf平滑参数，默认为True，idf=ln((文档总数+1)/(包含该词的文档数+1))+1，如果设为False，idf=ln(文档总数/包含该词的文档数)+1\nsublinear_tf: 默认为False，如果设为True，则替换tf为1 + log(tf)\nstop_words: 设置停用词，设为english将使用内置的英语停用词，设为一个list可自定义停用词，设为None不使用停用词，设为None且max_df∈[0.7, 1.0)将自动根据当前的语料库建立停用词表\n\"\"\"\ntfidf = TFIDF(min_df=2,\n           max_features=None,\n           strip_accents='unicode',\n           analyzer='word',\n           token_pattern=r'\\w{1,}',\n           ngram_range=(1, 3),  # 二元文法模型\n           use_idf=1,\n           smooth_idf=1,\n           sublinear_tf=1,\n           stop_words = 'english') # 去掉英文停用词\n\n# 合并训练和测试集以便进行TFIDF向量化操作\ndata_all = train_data + test_data\nlen_train = len(train_data)\n\ntfidf.fit(data_all)\ndata_all = tfidf.transform(data_all)\n# 恢复成训练集和测试集部分\ntrain_x = data_all[:len_train]\ntest_x = data_all[len_train:]\nprint('TF-IDF处理结束.')\n\nprint(\"train: \\n\", np.shape(train_x[0]))\nprint(\"test: \\n\", np.shape(test_x[0]))\n\n```\n\n    TF-IDF处理结束.\n    train: \n     (1, 810866)\n    test: \n     (1, 810866)\n\n\n### 朴素贝叶斯训练\n\n\n```python\n# 朴素贝叶斯训练\n\nfrom sklearn.naive_bayes import MultinomialNB as MNB\n\nmodel_NB = MNB() # (alpha=1.0, class_prior=None, fit_prior=True)\n# 为了在预测的时候使用\nmodel_NB.fit(train_x, label)\n\nfrom sklearn.model_selection import cross_val_score\nimport numpy as np\n\nprint(\"多项式贝叶斯分类器10折交叉验证得分:  \\n\", cross_val_score(model_NB, train_x, label, cv=10, scoring='roc_auc'))\nprint(\"\\n多项式贝叶斯分类器10折交叉验证得分: \", np.mean(cross_val_score(model_NB, train_x, label, cv=10, scoring='roc_auc')))\n\n```\n\n    多项式贝叶斯分类器10折交叉验证得分:  \n     [0.95134592 0.94728448 0.951648   0.94707712 0.95122816 0.94939968\n     0.95240704 0.95434432 0.94438528 0.94930816]\n    \n    多项式贝叶斯分类器10折交叉验证得分:  0.949842816\n\n\n\n```python\ntest_predicted = np.array(model_NB.predict(test_x))\nprint('保存结果...')\n\nsubmission_df = pd.DataFrame(data ={'id': test['id'], 'sentiment': test_predicted})\nprint(submission_df.head(10))\nsubmission_df.to_csv('/Users/jiangzl/Desktop/submission_br.csv',columns = ['id','sentiment'], index = False)\n\n# nb_output = pd.DataFrame(data=test_predicted, columns=['sentiment'])\n# nb_output['id'] = test['id']\n# nb_output = nb_output[['id', 'sentiment']]\n# nb_output.to_csv('nb_output.csv', index=False)\nprint('结束.')\n\n'''\n1.提交最终的结果到kaggle，AUC为：0.85728，排名300左右，50%的水平\n2. ngram_range = 3, 三元文法，AUC为0.85924\n'''\n```\n\n    保存结果...\n               id  sentiment\n    0  \"12311_10\"          1\n    1    \"8348_2\"          0\n    2    \"5828_4\"          1\n    3    \"7186_2\"          1\n    4   \"12128_7\"          1\n    5    \"2913_8\"          1\n    6    \"4396_1\"          0\n    7     \"395_2\"          0\n    8   \"10616_1\"          0\n    9    \"9074_9\"          1\n    结束.\n\n\n\n\n\n    '\\n1.提交最终的结果到kaggle，AUC为：0.85728，排名300左右，50%的水平\\n2. ngram_range = 3, 三元文法，AUC为0.85924\\n'\n\n\n\n### 逻辑回归\n\n\n```python\nfrom sklearn.linear_model import LogisticRegression as LR\nfrom sklearn.model_selection import GridSearchCV\n\n# 设定grid search的参数\ngrid_values = {'C': [1, 15, 30, 50]}  \n# grid_values = {'C': [30]}\n# 设定打分为roc_auc\n\"\"\"\npenalty: l1 or l2, 用于指定惩罚中使用的标准。\n\"\"\"\nmodel_LR = GridSearchCV(LR(penalty='l2', dual=True, random_state=0), grid_values, scoring='roc_auc', cv=20)\nmodel_LR.fit(train_x, label)\n# 20折交叉验证\n# GridSearchCV(cv=20, \n#         estimator=LR(C=1.0, \n#             class_weight=None, \n#             dual=True, \n#             fit_intercept=True, \n#             intercept_scaling=1, \n#             penalty='l2', \n#             random_state=0, \n#             tol=0.0001),\n#         fit_params={}, \n#         iid=True,\n#         n_jobs=1,\n#         param_grid={'C': [30]}, \n#         pre_dispatch='2*n_jobs',\n#         refit=True,\n#         scoring='roc_auc', \n#         verbose=0)\n\n# 输出结果\n# print(model_LR.grid_scores_, '\\n', model_LR.best_params_, model_LR.best_params_)\nprint(model_LR.cv_results_, '\\n', model_LR.best_params_, model_LR.best_score_)\n```\n\n    {'mean_fit_time': array([0.77368994, 1.95680232, 2.88316183, 3.50976259]), 'std_fit_time': array([0.05099312, 0.19345662, 0.39457327, 0.50422455]), 'mean_score_time': array([0.00273149, 0.0025926 , 0.00262785, 0.00249476]), 'std_score_time': array([0.0001698 , 0.00014623, 0.00014215, 0.00024111]), 'param_C': masked_array(data=[1, 15, 30, 50],\n                 mask=[False, False, False, False],\n           fill_value='?',\n                dtype=object), 'params': [{'C': 1}, {'C': 15}, {'C': 30}, {'C': 50}], 'split0_test_score': array([0.95273728, 0.95990784, 0.960192  , 0.9602816 ]), 'split1_test_score': array([0.96081408, 0.96953856, 0.96975104, 0.96994816]), 'split2_test_score': array([0.9583616 , 0.96794112, 0.96825856, 0.96836352]), 'split3_test_score': array([0.95249152, 0.96079104, 0.96123136, 0.96137984]), 'split4_test_score': array([0.96460288, 0.9721088 , 0.9724672 , 0.97263104]), 'split5_test_score': array([0.95881216, 0.96733184, 0.96779008, 0.96797184]), 'split6_test_score': array([0.95679232, 0.96563968, 0.96596736, 0.96606976]), 'split7_test_score': array([0.95171072, 0.96053248, 0.96105216, 0.96125952]), 'split8_test_score': array([0.95526656, 0.9604096 , 0.96051712, 0.96053248]), 'split9_test_score': array([0.94979328, 0.95777024, 0.95817472, 0.95834368]), 'split10_test_score': array([0.95965952, 0.9672192 , 0.9675264 , 0.96764672]), 'split11_test_score': array([0.95329024, 0.96009472, 0.96019712, 0.96021504]), 'split12_test_score': array([0.96268544, 0.97140224, 0.97184256, 0.97202944]), 'split13_test_score': array([0.9571968 , 0.96615936, 0.9666048 , 0.96676864]), 'split14_test_score': array([0.95916544, 0.96551936, 0.96583168, 0.96596992]), 'split15_test_score': array([0.96279296, 0.96956928, 0.96978176, 0.96979968]), 'split16_test_score': array([0.95332096, 0.96132352, 0.96161792, 0.96173568]), 'split17_test_score': array([0.94883328, 0.9570816 , 0.95749632, 0.95771136]), 'split18_test_score': array([0.9528448 , 0.96074496, 0.96114176, 0.9612672 ]), 'split19_test_score': array([0.96429824, 0.97186048, 0.972032  , 0.97212416]), 'mean_test_score': array([0.9567735 , 0.9646473 , 0.9649737 , 0.96510246]), 'std_test_score': array([0.0046911 , 0.00476416, 0.00475249, 0.00475557]), 'rank_test_score': array([4, 3, 2, 1], dtype=int32), 'split0_train_score': array([0.99254593, 1.        , 1.        , 1.        ]), 'split1_train_score': array([0.99230078, 1.        , 1.        , 1.        ]), 'split2_train_score': array([0.9923811, 1.       , 1.       , 1.       ]), 'split3_train_score': array([0.9924227, 1.       , 1.       , 1.       ]), 'split4_train_score': array([0.9923401, 1.       , 1.       , 1.       ]), 'split5_train_score': array([0.9924475, 1.       , 1.       , 1.       ]), 'split6_train_score': array([0.99238184, 1.        , 1.        , 1.        ]), 'split7_train_score': array([0.99249388, 1.        , 1.        , 1.        ]), 'split8_train_score': array([0.99257082, 1.        , 1.        , 1.        ]), 'split9_train_score': array([0.99253744, 1.        , 1.        , 1.        ]), 'split10_train_score': array([0.99235201, 1.        , 1.        , 1.        ]), 'split11_train_score': array([0.99243953, 1.        , 1.        , 1.        ]), 'split12_train_score': array([0.99236668, 1.        , 1.        , 1.        ]), 'split13_train_score': array([0.99248181, 1.        , 1.        , 1.        ]), 'split14_train_score': array([0.99254685, 1.        , 1.        , 1.        ]), 'split15_train_score': array([0.99240575, 1.        , 1.        , 1.        ]), 'split16_train_score': array([0.99240521, 1.        , 1.        , 1.        ]), 'split17_train_score': array([0.99248037, 1.        , 1.        , 1.        ]), 'split18_train_score': array([0.99243375, 1.        , 1.        , 1.        ]), 'split19_train_score': array([0.99242053, 1.        , 1.        , 1.        ]), 'mean_train_score': array([0.99243773, 1.        , 1.        , 1.        ]), 'std_train_score': array([7.34564551e-05, 0.00000000e+00, 2.48253415e-17, 2.48253415e-17])} \n     {'C': 50} 0.965102464\n\n\n\n```python\nmodel_LR = LR(penalty='l2', dual=True, random_state=0)\nmodel_LR.fit(train_x, label)\n\ntest_predicted = np.array(model_LR.predict(test_x))\nprint('保存结果...')\nsubmission_df = pd.DataFrame(data ={'id': test['id'], 'sentiment': test_predicted})\nprint(submission_df.head(10))\nsubmission_df.to_csv('/Users/jiangzl/Desktop/submission_br.csv',columns = ['id','sentiment'], index = False)\nprint('结束.')\n\n'''\n1. 提交最终的结果到kaggle，AUC为：0.88956，排名260左右，比之前贝叶斯模型有所提高\n2. 三元文法，AUC为0.89076\n'''\n```\n\n    保存结果...\n               id  sentiment\n    0  \"12311_10\"          1\n    1    \"8348_2\"          0\n    2    \"5828_4\"          1\n    3    \"7186_2\"          1\n    4   \"12128_7\"          1\n    5    \"2913_8\"          1\n    6    \"4396_1\"          0\n    7     \"395_2\"          0\n    8   \"10616_1\"          0\n    9    \"9074_9\"          1\n    结束.\n\n\n\n\n\n    '\\n1. 提交最终的结果到kaggle，AUC为：0.88956，排名260左右，比之前贝叶斯模型有所提高\\n2. 三元文法，AUC为0.89076\\n'\n\n\n\n## 3.Word2vec向量\n\n神经网络语言模型L = SUM[log(p(w|contect(w))]，即在w的上下文下计算当前词w的概率，由公式可以看到，我们的核心是计算p(w|contect(w)， Word2vec给出了构造这个概率的一个方法。\n\n\n```python\nimport gensim\nimport nltk\nfrom nltk.corpus import stopwords\n\ntokenizer = nltk.data.load('/opt/data/nlp/nltk_data/tokenizers/punkt/english.pickle')\n    \ndef review_to_wordlist(review, remove_stopwords=False):\n    # review = BeautifulSoup(review, \"html.parser\").get_text()\n    review_text = re.sub(\"[^a-zA-Z]\",\" \", review)\n\n    words = review_text.lower().split()\n\n    if remove_stopwords:\n        stops = set(stopwords.words(\"english\"))\n        words = [w for w in words if not w in stops]\n    # print(words)\n    return(words)\n\n\ndef review_to_sentences(review, tokenizer, remove_stopwords=False):\n    '''\n    1. 将评论文章，按照句子段落来切分(所以会比文章的数量多很多)\n    2. 返回句子列表，每个句子由一堆词组成\n    '''\n    review = BeautifulSoup(review, \"html.parser\").get_text()\n    # raw_sentences 句子段落集合\n    raw_sentences = tokenizer.tokenize(review)\n    # print(raw_sentences)\n    \n    sentences = []\n    for raw_sentence in raw_sentences:\n        if len(raw_sentence) > 0:\n            # 获取句子中的词列表\n            sentences.append(review_to_wordlist(raw_sentence, remove_stopwords))\n    return sentences\n\n\nsentences = []\nfor i, review in enumerate(train[\"review\"]):\n    # print(i, review)\n    sentences += review_to_sentences(review, tokenizer, True)\n\n```\n\n\n```python\nprint(np.shape(train[\"review\"]))\nprint(np.shape(sentences))\n```\n\n    (25000,)\n    (267192,)\n\n\n\n```python\nunlabeled_train = pd.read_csv(\"%s/%s\" % (root_dir, \"unlabeledTrainData.tsv\"), header=0, delimiter=\"\\t\", quoting=3 )\nfor review in unlabeled_train[\"review\"]:\n    sentences += review_to_sentences(review, tokenizer)\nprint('预处理 unlabeled_train data...')\n```\n\n    预处理 unlabeled_train data...\n\n\n\n```python\nprint(np.shape(train_data))\nprint(np.shape(sentences))\n```\n\n    (25000,)\n    (1035107,)\n\n\n> 构建word2vec模型\n\n\n```python\nimport time\nfrom gensim.models import Word2Vec\n# 模型参数\nnum_features = 300    # Word vector dimensionality                      \nmin_word_count = 40   # Minimum word count                        \nnum_workers = 4       # Number of threads to run in parallel\ncontext = 10          # Context window size                                                                                    \ndownsampling = 1e-3   # Downsample setting for frequent words\n\n```\n\n\n```python\n%%time\n# 训练模型\nprint(\"训练模型中...\")\nmodel = Word2Vec(sentences, workers=num_workers, \\\n            size=num_features, min_count=min_word_count, \\\n            window=context, sample=downsampling)\nprint(\"训练完成\")\n```\n\n    训练模型中...\n    训练完成\n    CPU times: user 7min 12s, sys: 6.7 s, total: 7min 19s\n    Wall time: 2min 29s\n\n\n\n```python\nprint('保存模型...')\nmodel.init_sims(replace=True)\nmodel_name = \"%s/%s\" % (root_dir, \"300features_40minwords_10context\")\nmodel.save(model_name)\nprint('保存结束')\n```\n\n    保存模型...\n    保存结束\n\n\n> 预处理\n\n\n```python\nmodel.wv.doesnt_match(\"man woman child kitchen\".split())\n```\n\n\n\n\n    'kitchen'\n\n\n\n\n```python\nmodel.wv.doesnt_match(\"france england germany berlin\".split())\n```\n\n\n\n\n    'berlin'\n\n\n\n\n```python\nmodel.wv.doesnt_match(\"paris berlin london austria\".split())\n```\n\n\n\n\n    'paris'\n\n\n\n\n```python\n# help(model.wv.most_similar)\nmodel.wv.most_similar(\"man\", topn=5)\n```\n\n\n\n\n    [('woman', 0.5622519850730896),\n     ('lady', 0.5539723634719849),\n     ('lad', 0.5375600457191467),\n     ('men', 0.4897556006908417),\n     ('monk', 0.48445409536361694)]\n\n\n\n\n```python\nmodel.wv.most_similar(\"queen\", topn=5)\n```\n\n\n\n\n    [('princess', 0.6005384922027588),\n     ('bride', 0.5296590328216553),\n     ('queens', 0.5233569145202637),\n     ('eva', 0.5130444765090942),\n     ('brunette', 0.505348265171051)]\n\n\n\n\n```python\nmodel.wv.most_similar(\"awful\", topn=5)\n```\n\n\n\n\n    [('terrible', 0.7551479935646057),\n     ('abysmal', 0.7124387621879578),\n     ('horrible', 0.7055309414863586),\n     ('atrocious', 0.6951155066490173),\n     ('horrendous', 0.6731454730033875)]\n\n\n\n\n```python\nmodel.wv.most_similar(positive=['woman', 'king'], negative=['man'], topn=1)\n```\n\n\n\n\n    [('princess', 0.4474681615829468)]\n\n\n\n> 使用Word2vec特征\n\n\n```python\ndef makeFeatureVec(words, model, num_features):\n    '''\n    对段落中的所有词向量进行取平均操作\n    '''\n    featureVec = np.zeros((num_features,), dtype=\"float32\")\n    nwords = 0.\n\n    # Index2word包含了词表中的所有词，为了检索速度，保存到set中\n    index2word_set = set(model.wv.index2word)\n    for word in words:\n        if word in index2word_set:\n            nwords = nwords + 1.\n            featureVec = np.add(featureVec, model[word])\n\n    # 取平均\n    featureVec = np.divide(featureVec, nwords)\n    return featureVec\n\n\ndef getAvgFeatureVecs(reviews, model, num_features):\n    '''\n    给定一个文本列表，每个文本由一个词列表组成，返回每个文本的词向量平均值\n    '''\n    counter = 0\n    reviewFeatureVecs = np.zeros((len(reviews), num_features), dtype=\"float32\")\n\n    for review in reviews:\n        if counter % 5000 == 0:\n            print(\"Review %d of %d\" % (counter, len(reviews)))\n\n        reviewFeatureVecs[counter] = makeFeatureVec(review, model, num_features)\n        counter = counter + 1\n\n    return reviewFeatureVecs\n```\n\n\n```python\n%time trainDataVecs = getAvgFeatureVecs(train_data, model, num_features)\nprint(np.shape(trainDataVecs))\n```\n\n    Review 0 of 25000\n\n\n    /Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/ipykernel_launcher.py:13: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n      del sys.path[0]\n\n\n    Review 5000 of 25000\n    Review 10000 of 25000\n    Review 15000 of 25000\n    Review 20000 of 25000\n    CPU times: user 5min 27s, sys: 4.14 s, total: 5min 31s\n    Wall time: 5min 58s\n    (25000, 300)\n\n\n\n```python\n%time testDataVecs = getAvgFeatureVecs(test_data, model, num_features)\nprint(np.shape(testDataVecs))\n```\n\n    Review 0 of 25000\n\n\n    /Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/ipykernel_launcher.py:13: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n      del sys.path[0]\n\n\n    Review 5000 of 25000\n    Review 10000 of 25000\n    Review 15000 of 25000\n    Review 20000 of 25000\n    CPU times: user 5min 10s, sys: 3.6 s, total: 5min 14s\n    Wall time: 5min 30s\n    (25000, 300)\n\n\n### 高斯贝叶斯+Word2vec训练\n\n\n```python\nfrom sklearn.naive_bayes import GaussianNB as GNB\n\nmodel_GNB = GNB()\nmodel_GNB.fit(trainDataVecs, label)\n\nfrom sklearn.cross_validation import cross_val_score\nimport numpy as np\n\nprint(\"高斯贝叶斯分类器10折交叉验证得分: \", np.mean(cross_val_score(model_GNB, trainDataVecs, label, cv=10, scoring='roc_auc')))\n\nprint('保存结果...')\nresult = model_GNB.predict( testDataVecs )\nsubmission_df = pd.DataFrame(data ={'id': test['id'], 'sentiment': result})\nprint(submission_df.head(10))\nsubmission_df.to_csv('/Users/jiangzl/Desktop/gnb_word2vec.csv',columns = ['id','sentiment'], index = False)\nprint('结束.')\n\n\"\"\"\n从验证结果来看，没有超过基于TF-IDF多项式贝叶斯模型\n\"\"\"\n```\n\n    高斯贝叶斯分类器10折交叉验证得分:  0.6163932159999999\n    保存结果...\n               id  sentiment\n    0  \"12311_10\"          0\n    1    \"8348_2\"          0\n    2    \"5828_4\"          1\n    3    \"7186_2\"          0\n    4   \"12128_7\"          0\n    5    \"2913_8\"          0\n    6    \"4396_1\"          0\n    7     \"395_2\"          1\n    8   \"10616_1\"          1\n    9    \"9074_9\"          1\n    结束.\n\n\n\n\n\n    '\\n从验证结果来看，没有超过基于TF-IDF多项式贝叶斯模型\\n'\n\n\n\n### 随机森林+Word2vec训练\n\n\n```python\nfrom sklearn.ensemble import RandomForestClassifier\n\nforest = RandomForestClassifier( n_estimators = 100, n_jobs=2)\n\nprint(\"Fitting a random forest to labeled training data...\")\n%time forest = forest.fit( trainDataVecs, label )\nprint(\"随机森林分类器10折交叉验证得分: \", np.mean(cross_val_score(forest, trainDataVecs, label, cv=10, scoring='roc_auc')))\n\n# 测试集\nresult = forest.predict( testDataVecs )\n\nprint('保存结果...')\nsubmission_df = pd.DataFrame(data ={'id': test['id'], 'sentiment': result})\nprint(submission_df.head(10))\nsubmission_df.to_csv('/Users/jiangzl/Desktop/rf_word2vec.csv',columns = ['id','sentiment'], index = False)\nprint('结束.')\n\n\"\"\"\n改用随机森林之后，效果有提升，但是依然没有超过基于TF-IDF多项式贝叶斯模型\n\"\"\"\n```\n\n    Fitting a random forest to labeled training data...\n    CPU times: user 43.8 s, sys: 347 ms, total: 44.2 s\n    Wall time: 23.1 s\n    随机森林分类器10折交叉验证得分:  0.6428176640000001\n    保存结果...\n               id  sentiment\n    0  \"12311_10\"          1\n    1    \"8348_2\"          1\n    2    \"5828_4\"          0\n    3    \"7186_2\"          1\n    4   \"12128_7\"          0\n    5    \"2913_8\"          0\n    6    \"4396_1\"          0\n    7     \"395_2\"          0\n    8   \"10616_1\"          1\n    9    \"9074_9\"          1\n    结束.\n\n\n\n\n\n    '\\n改用随机森林之后，效果有提升，但是依然没有超过基于TF-IDF多项式贝叶斯模型\\n'\n\n\n\n\n```python\n# 加载训练好的词向量\n\nfrom gensim.models.word2vec import Word2Vec\n\nmodel = Word2Vec.load_word2vec_format(\"vector.txt\", binary=False)  # C text format\n# model = Word2Vec.load_word2vec_format(\"vector.bin\", binary=True)  # C\n```\n\n\n```python\n# 加载 google 的词向量，查看单词之间关系\n\nfrom gensim.models.word2vec import Word2Vec \nmodel = Word2Vec.load_word2vec_format(\"GoogleNews-vectors-negative300.bin\", binary=True)\n```\n\n\n```python\n# 测试预测效果\n\nprint(model.most_similar(positive=[\"woman\", \"king\"], negative=[\"man\"], topn=5))\nprint(model.most_similar(positive=[\"biggest\", \"small\"], negative=[\"big\"], topn=5))\nprint(model.most_similar(positive=[\"ate\", \"speak\"], negative=[\"eat\"], topn=5))\n```\n\n\n```python\nimport numpy as np\n\nwith open(\"food_words.txt\", \"r\") as infile:\n    food_words = infile.readlines()\n    \nwith open(\"sports_words.txt\", \"r\") as infile:\n    food_words = infile.readlines()\n    \nwith open(\"weather_words.txt\", \"r\") as infile:\n    food_words = infile.readlines()\n    \ndef getWordVecs(words):\n    vec = []\n    for word in words:\n        word = word.replace(\"\\n\", \"\")\n        try:\n            vecs.append(model[word].reshape((1, 300)))\n        except KeyError:\n            continue\n    \n    # numpy提供了numpy.concatenate((a1,a2,...), axis=0)函数。能够一次完成多个数组的拼接\n    \"\"\"\n    >>> a=np.array([1,2,3])\n    >>> b=np.array([11,22,33])\n    >>> c=np.array([44,55,66])\n    >>> np.concatenate((a,b,c),axis=0)  # 默认情况下，axis=0可以不写\n    array([ 1,  2,  3, 11, 22, 33, 44, 55, 66]) #对于一维数组拼接，axis的值不影响最后的结果\n    \"\"\"\n    vecs = np.concatenate(vecs)\n    return np.array(vecs, dtype=\"float\")\n\nfood_vecs = getWordVecs(food_words)\nsports_vecs = getWordVecs(sports_words)\nweather_vecs = getWordVecs(weather_words)\n```\n\n\n```python\n# 利用 TSNE 和 matplotlib 对分类结果进行可视化处理\n\nfrom sklearn.manifold import TSEN\nimport matplotlib.pyplot as plt\n\nts = TSEN(2)\nreduced_vecs = ts.fit_transform(np.concatenate((food_vecs, sports_vecs, weather_vecs)))\n\nfor i in range(len(reduced_vecs)):\n    if i < len(food_vecs):\n        color = \"b\"\n    elif i >= len(food_vecs) and i <(len(food_vecs)+len(sports_vecs)):\n        color = \"r\"\n    else:\n        color = \"g\"\n    \n    plt.plot(reduced_vecs[i, 0], reduced_vecs[i, 1], marker=\"0\", color=color, marksize=8)\n```\n\n\n```python\n# 首先，我们导入数据并构建 Word2Vec 模型：\n\nfrom sklearn.cross_validation import train_ _test_ _split\nfrom gensim.models.word2vec import Word2Vec\n\nwith open('twitter.data/pos_ tweets.txt', 'r') as infile:\n    pos_tweets= infile.readlines()\n\nwith open(' twitter_ data/neg_ tweets.txt', 'r') as infile:\n    neg_ _tweets = infile.readlines()\n\n# use 1for positive sentiment,0 for negative\nY= np.concatenate((np.ones( len (pos_tweets )) ，np.zeros(len(neg_tweets))))\n\nx_train,x_test,y_train,y_test = train_test_split(np.concatenate((pos_tweets, neg_tweets)), y, test_size=0.2)\n# Do some very minor text preprocessing\n\ndef cleanText(corpus):\n    corpus= [z.lower( ).replace(' \\n' , '').split() for z in corpus]\n    return corpus\n\nx_ train= cleanText(x_ train)\nx_ test= cleanText (x_ _test)\n\nn _dim= 300\n#Initialize model and build vocab\nimdb_w2v= Word2Vec(size=n dim, min_count=10)\nimdb_w2v.build_vocab(x_ _train)\n#Train the model over train_ _reviews (this may take several minutes)\nimdb_w2v.train( x_train)\n```\n\n\n```python\n# 接下来，为了利用下面的函数获得推文中所有词向量的平均值，我们必须构建作为输入文本的词向量。\n\ndef buildWordVector(text, size):\n    vec = np.zeros(size).reshape((1，size))\n    count= 0.\n\n    for word in text :\n        try:\n            vec += imdb_w2v[word].reshape( (1，size) )\n            count += 1.\n        except KeyError:\n            continue\n    if count != 0:\n        vec 1'= count\n    return vec\n```\n\n\n```python\n# 调整数据集的量纲是数据标准化处理的一部分，我们通常将数据集转化成服从均值为零的高斯分布，这说明数值大于均值表示乐观，反之则表示悲观。为了使模型更有效，许多机器学习模型需要预先处理数据集的量纲，特别是文本分类器这类具有许多变量的模型。\n\nfrom sklearn.preprocessing import scale\n\ntrain_vecs = np.concatenate([buildWordVector(z ，n_dim) for z in x_train])\ntrain_vecs= scale(train_vecs)\n\n# Train word2vec on test tweets\nimdb_w2v.train(x_test)\n\n```\n\n\n```python\n# 最后我们需要建立测试集向量并对其标准化处理：\n\n#Build test tweet vectors then scale\ntest_vecs = np.concatenate( [buildWordVector( Z，n _dim) for z in x _test ])\ntest_vecs = scale(test_vecs)\n\n```\n\n\n```python\n\"\"\"\n接下来我们想要通过计算测试集的预测精度和 ROC 曲线来验证分类器的有效性。 ROC 曲线衡量当模型参数调整的时候，其真阳性率和假阳性率的变化情况。在我们的案例中，我们调整的是分类器模型截断阈值的概率。一般来说，ROC 曲线下的面积（AUC）越大，该模型的表现越好。你可以在这里找到更多关于 ROC 曲线的资料\n\n（https://en.wikipedia.org/wiki/Receiver_operating_characteristic）\n\n在这个案例中我们使用罗吉斯回归的随机梯度下降法作为分类器算法。\n\"\"\"\n\n#Use classification algorithm (i.e.Stochastic Logistic Regression) on training set, then assess model performance on test set\n\nfrom sklearn.linear model import SGDClassifier\nlr = SGDClassifier(loss='log' ，penalty='11' )\nlr.fit(train_vecs, y_train)\nprint' Test Accuracy: %.2f' % r.score(test vecs, y_test )\n\n\n\n```\n\n\n```python\n# 随后我们利用 matplotlib 和 metric 库来构建 ROC 曲线\n\n#Crea t e ROC curve\nfrom sklearn.metrics import roc_curve, auc\nimport matplotlib.pyplot as plt\n\npred_probas = lr.predict_proba(test_vecs)[:, 1]\n\nfpr, tpr, _ = roc_curve(y_test, pred_probas )\nroc_auc = auc(fpr, tpr)\n\nplt.plot(fpr,tpr,label='area = %.2f' % roc_ auc)\nplt.plot([0,1]，[0，1],'k--')\nplt. xlim( [0. 0 ，1. 0 ])\nplt.ylim([0.0, 1.05])\nplt.legend(loc='lower right')\nplt.show()\n```\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/官方教程翻译/0.md",
    "content": "# 描述\n\n在本教程竞赛中，我们对情感分析进行了一些“深入”研究。谷歌的 Word2Vec 是一种受深度学习启发的方法，专注于单词的含义。 Word2Vec 试图理解单词之间的意义和语义关系。它的工作方式类似于深度方法，例如循环神经网络或深度神经网络，但计算效率更高。本教程重点介绍用于情感分析的 Word2Vec。\n\n情感分析是机器学习中的一个挑战性课题。人们用语言来表达自己的情感，这种语言经常被讽刺，二义性和文字游戏所掩盖，所有这些都会对人类和计算机产生误导。还有另一个 Kaggle 电影评论情绪分析竞赛。在本教程中，我们将探讨如何将 Word2Vec 应用于类似的问题。\n\n在过去的几年里，深度学习在新闻中大量出现，甚至进入纽约时报的头版。这些机器学习技术受到人类大脑架构的启发，并且由于计算能力的最新进展而实现，由于图像识别，语音处理和自然语言任务的突破性结果，已经成为浪潮。最近，深度学习方法赢得了几项 Kaggle 比赛，包括药物发现任务和猫狗图像识别。\n\n### 教程概览\n\n本教程将帮助你开始使用 Word2Vec 进行自然语言处理。 它有两个目标：\n\n基本自然语言处理：本教程的第 1 部分适用于初学者，涵盖了本教程后续部分所需的基本自然语言处理技术。\n\n文本理解的深度学习：在第 2 部分和第 3 部分中，我们深入研究如何使用 Word2Vec 训练模型以及如何使用生成的单词向量进行情感分析。\n\n由于深度学习是一个快速发展的领域，大量的工作尚未发表，或仅作为学术论文存在。 本教程的第 3 部分比说明性更具探索性 - 我们尝试了几种使用 Word2Vec 的方法，而不是为你提供使用输出的方法。\n\n为了实现这些目标，我们依靠 IMDB 情绪分析数据集，其中包含 100,000 个多段电影评论，包括正面和负面。\n\n### 致谢\n\n此数据集是与以下出版物一起收集的：\n\n> [Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). \"Learning Word Vectors for Sentiment Analysis.\" The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).](http://ai.stanford.edu/~ang/papers/acl11-WordVectorsSentimentAnalysis.pdf)\n\n如果你将数据用于任何研究应用，请发送电子邮件给该论文的作者。 该教程由 [Angela Chapman](http://www.linkedin.com/pub/angela-chapman/5/330/b97) 在 2014 年夏天在 Kaggle 实习期间开发。\n\n### 什么是深度学习\n\n术语“深度学习”是在2006年创造的，指的是具有多个非线性层并且可以学习特征层次结构的机器学习算法[1]。\n\n大多数现代机器学习依赖于特征工程或某种级别的领域知识来获得良好的结果。 在深度学习系统中，情况并非如此 - 相反，算法可以自动学习特征层次结构，这些层次结构表示抽象级别增加的对象。 虽然许多深度学习算法的基本要素已存在多年，但由于计算能力的提高，计算硬件成本的下降以及机器学习研究的进步，它们目前正日益受到欢迎。\n\n深度学习算法可以按其架构（前馈，反馈或双向）和训练协议（监督，混合或无监督）进行分类[2]。\n\n一些好的背景材料包括：\n\n[1] [\"Deep Learning for Signal and Information Processing\", by Li Deng and Dong Yu (out of Microsoft)](http://cs.tju.edu.cn/web/docs/2013-Deep%20Learning%20for%20Signal%20and%20Information%20Processing.pdf)\n\n[2] [\"Deep Learning Tutorial\" (2013 Presentation by Yann LeCun and Marc'Aurelio Ranzato)](http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf)\n\n### Word2Vec 适合哪里？\n\nWord2Vec的工作方式类似于深度方法，如循环神经网络或深度神经网络，但它实现了某些算法，例如分层 softmax，使计算效率更高。\n\n对于 Word2Vec 以及本文的更多信息，请参阅本教程的第 2 部分，以及这篇论文：[Efficient Estimation of Word Representations in Vector Space](http://arxiv.org/pdf/1301.3781v3.pdf)\n\n在本教程中，我们使用混合方法进行训练 - 由无监督的片段（Word2Vec）和监督学习（随机森林）组成。\n\n### 库和包\n\n以下列表并不是详尽无遗的。\n\nPython 中：\n\nTheano 提供非常底层的基本功能，用于构建深度学习系统。 你还可以在他们的网站上找到一些很好的教程。\nCaffe 是 Berkeley 视觉和学习中心的深度学习框架。\nPylearn2 包装了 Theano，似乎更加用户友好。\nOverFeat 用于赢得 Kaggle 猫和狗的比赛。\n\nLua 中：\n\nTorch 是一个受欢迎的包，并附带一个教程。\n\nR 中：\n\n截至 2014 年 8 月，有一些软件包刚刚开始开发，但没有可以在教程中使用的，非常成熟的包。\n\n其他语言也可能有很好的包，但我们还没有对它们进行过研究。\n\n### 更多教程\n\nO'Reilly 博客有一系列深度学习文章和教程：\n\n[什么是深度学习，为什么要关心？](http://radar.oreilly.com/2014/07/what-is-deep-learning-and-why-should-you-care.html)\n[如何构建和运行你的第一个深度学习网络](http://radar.oreilly.com/2014/07/how-to-build-and-run-your-first-deep-learning-network.html)\n[网络广播：如何起步深入学习计算机视觉](http://www.oreilly.com/pub/e/3121)\n\n还有[几个使用 Theano 的教程](http://deeplearning.net/tutorial/)。\n\n如果你想从零开始创建神经网络，请查看 Geoffrey Hinton 的 [Coursera 课程](https://www.coursera.org/course/neuralnets)。\n\n对于 NLP，请查看斯坦福大学最近的这个讲座：[没有魔法的 NLP 的深度学习](http://techtalks.tv/talks/deep-learning-for-nlp-without-magic-part-1/58414/)。\n\n这本免费的在线书籍还介绍了用于深度学习的神经网络：[神经网络和深度学习](http://neuralnetworksanddeeplearning.com/)。\n\n### 配置你的系统\n\n如果你之前没有安装过 Python 模块，请查看[此教程](http://programminghistorian.org/lessons/installing-python-modules-pip)，提供了从终端（在 Mac / Linux 中）或命令提示符（在 Windows 中）安装模块的指南。\n\n运行本教程需要安装以下软件包。 在大多数（或所有）情况下，我们建议你使用[`pip`](https://pypi.python.org/pypi/pip)来安装软件包。\n\n*   [pandas](http://pandas.pydata.org/pandas-docs/stable/install.html)\n*   [numpy](http://docs.scipy.org/doc/numpy/user/install.html)\n*   scipy\n*   [scikit-learn ](http://scikit-learn.org/stable/install.html)\n*   [Beautiful Soup](http://www.crummy.com/software/BeautifulSoup/bs4/doc/)\n*   [NLTK](http://www.nltk.org/install.html)\n*   [Cython](http://docs.cython.org/src/quickstart/install.html)\n*   [gensim](http://radimrehurek.com/gensim/install.html)\n\nWord2Vec 可以在`gensim`包中找到。 请注意，到目前为止，我们只在 Mac OS X 上成功运行了本教程，而不是 Windows。\n\n如果你在 Mac Mavericks（10.9）上安装软件包时遇到问题，[本教程](http://hackercodex.com/guide/python-development-environment-on-mac-osx/)包含正确配置系统的说明。\n\n本教程中的代码是为 Python 2.7 开发的。\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/官方教程翻译/1.md",
    "content": "## 第一部分：写给入门者的词袋\n\n### 什么是 NLP\n\nNLP（自然语言处理）是一组用于处理文本问题的技术。这个页面将帮助你从加载和清理IMDB电影评论来起步，然后应用一个简单的[词袋](http://en.wikipedia.org/wiki/Bag-of-words_model)模型，来获得令人惊讶的准确预测，评论是点赞还是点踩。\n\n### 在你开始之前\n\n本教程使用 Python。如果你之前没有使用过 Python，我们建议你前往[泰坦尼克号竞赛 Python 教程](http://www.kaggle.com/c/titanic-gettingStarted)，熟悉一下（查看随机森林介绍）。\n\n如果你已熟悉 Python 并使用基本的 NLP 技术，则可能需要跳到第 2 部分。\n\n本教程的这一部分不依赖于平台。在本教程中，我们将使用各种 Python 模块进行文本处理，深度学习，随机森林和其他应用。详细信息请参阅“配置你的系统”页面。\n\n有很多很好的教程，以及实际上用 Python 写的关于 NLP 和文本处理的[整本书](http://www.nltk.org/book/)。本教程绝不是详尽无遗的 - 只是为了帮助你以电影评论起步。\n\n### 代码\n\n第 1 部分的教程代码就在[这里](https://github.com/wendykan/DeepLearningMovies/blob/master/BagOfWords.py)。\n\n### 读取数据\n\n可以从“数据”页面下载必要的文件。你需要的第一个文件是`unlabeledTrainData`，其中包含 25,000 个 IMDB 电影评论，每个评论都带有正面或负面情感标签。\n\n接下来，将制表符分隔文件读入 Python。为此，我们可以使用泰坦尼克号教程中介绍的`pandas`包，它提供了`read_csv`函数，用于轻松读取和写入数据文件。如果你之前没有使用过`pandas`，则可能需要安装它。\n\n```py\n# 导入 pandas 包，然后使用 \"read_csv\" 函数读取标记的训练数据\nimport pandas as pd       \ntrain = pd.read_csv(\"labeledTrainData.tsv\", header=0, \\\n                    delimiter=\"\\t\", quoting=3)\n```\n\n这里，`header=0`表示文件的第一行包含列名，`delimiter=\\t`表示字段由制表符分隔，`quoting=3`让 Python 忽略双引号，否则试图读取文件时，可能会遇到错误。\n\n我们可以确保读取 25,000 行和 3 列，如下所示：\n\n```py\n>>> train.shape\n(25000, 3)\n\n>>> train.columns.values\narray([id, sentiment, review], dtype=object)\n```\n\n这三列被称为`\"id\"`，`\"sentiment\"`和`\"array\"`。 现在你已经读取了培训集，请查看几条评论：\n\n```py\nprint train[\"review\"][0]\n```\n\n提醒一下，这将显示名为`\"review\"`的列中的第一个电影评论。 你应该看到一个像这样开头的评论：\n\n```py\n\"With all this stuff going down at the moment with MJ i've started listening to his music, watching the odd documentary here and there, watched The Wiz and watched Moonwalker again. Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent. Moonwalker is part biography, part feature film which i remember going to see at the cinema when it was originally released. Some of it has subtle messages about MJ's feeling towards the press and also the obvious message of drugs are bad m'kay. <br/><br/>...\"\n```\n\n有 HTML 标签，如`\"<br/>\"`，缩写，标点符号 - 处理在线文本时的所有常见问题。 花一些时间来查看训练集中的其他评论 - 下一节将讨论如何为机器学习整理文本。\n\n### 数据清理和文本预处理\n\n删除 HTML 标记：`BeautifulSoup`包\n\n首先，我们将删除 HTML 标记。 为此，我们将使用[`BeautifulSoup`库](http://www.crummy.com/software/BeautifulSoup/bs4/doc/)。 如果你没有安装，请从命令行（不是从 Python 内部）执行以下操作：\n\n```\n$ sudo pip install BeautifulSoup4\n```\n\n然后，从 Python 中加载包并使用它从评论中提取文本：\n\n```py\n# Import BeautifulSoup into your workspace\nfrom bs4 import BeautifulSoup             \n\n# Initialize the BeautifulSoup object on a single movie review     \nexample1 = BeautifulSoup(train[\"review\"][0])  \n\n# Print the raw review and then the output of get_text(), for \n# comparison\nprint train[\"review\"][0]\nprint example1.get_text()\n```\n\n调用`get_text()`会为你提供不带标签的评论文本。如果你浏览`BeautifulSoup`文档，你会发现它是一个非常强大的库 - 比我们对此数据集所需的功能更强大。但是，使用正则表达式删除标记并不是一种可靠的做法，因此即使对于像这样简单的应用程序，通常最好使用像`BeautifulSoup`这样的包。\n\n处理标点符号，数字和停止词：NLTK 和正则表达式\n\n在考虑如何清理文本时，我们应该考虑我们试图解决的数据问题。对于许多问题，删除标点符号是有意义的。另一方面，在这种情况下，我们正在解决情感分析问题，并且有可能`\"!!!\"`或者`\":-(\"`可以带有情感，应该被视为单词。在本教程中，为简单起见，我们完全删除了标点符号，但这是你可以自己玩的东西。\n\n与之相似，在本教程中我们将删除数字，但还有其他方法可以处理它们，这些方法同样有意义。例如，我们可以将它们视为单词，或者使用占位符字符串（例如`\"NUM\"`）替换它们。\n\n要删除标点符号和数字，我们将使用一个包来处理正则表达式，称为`re`。Python 内置了该软件包；无需安装任何东西。对于正则表达式如何工作的详细说明，请参阅[包文档](https://docs.python.org/2/library/re.html#)。现在，尝试以下方法：\n\n```py\nimport re\n# 使用正则表达式执行查找和替换\nletters_only = re.sub(\"[^a-zA-Z]\",           # 要查找的模式串\n                      \" \",                   # 要替换成的模式串\n                      example1.get_text() )  # 要从中查找的字符串\nprint letters_only\n```\n\n正则表达式的完整概述超出了本教程的范围，但是现在知道`[]`表示分组成员而`^`表示“不”就足够了。 换句话说，上面的`re.sub()`语句说：“查找任何不是小写字母（`a-z`）或大写字母（`A-Z`）的内容，并用空格替换它。”\n\n我们还将我们的评论转换为小写并将它们分成单个单词（在 NLP 术语中称为“[分词](http://en.wikipedia.org/wiki/Tokenization)”）：\n\n```py\nlower_case = letters_only.lower()        # 转换为小写\nwords = lower_case.split()               # 分割为单词\n```\n\n最后，我们需要决定如何处理那些没有多大意义的经常出现的单词。 这样的词被称为“停止词”；在英语中，它们包括诸如“a”，“and”，“is”和“the”之类的单词。方便的是，Python 包中内置了停止词列表。让我们从 Python 自然语言工具包（NLTK）导入停止词列表。 如果你的计算机上还没有该库，则需要安装该库；你还需要安装附带的数据包，如下所示：\n\n```py\nimport nltk\nnltk.download()  # 下载文本数据集，包含停止词\n```\n\n现在我们可以使用`nltk`来获取停止词列表：\n\n```py\nfrom nltk.corpus import stopwords # 导入停止词列表\nprint stopwords.words(\"english\") \n```\n\n这将允许你查看英语停止词列表。 要从我们的电影评论中删除停止词，请执行：\n\n```py\n# 从 \"words\" 中移除停止词\nwords = [w for w in words if not w in stopwords.words(\"english\")]\nprint words\n```\n\n这会查看`words`列表中的每个单词，并丢弃在停止词列表中找到的任何内容。 完成所有这些步骤后，你的评论现在应该是这样的：\n\n```py\n[u'stuff', u'going', u'moment', u'mj', u've', u'started', u'listening', u'music', u'watching', u'odd', u'documentary', u'watched', u'wiz', u'watched', u'moonwalker', u'maybe', u'want', u'get', u'certain', u'insight', u'guy', u'thought', u'really', u'cool', u'eighties', u'maybe', u'make', u'mind', u'whether', u'guilty', u'innocent', u'moonwalker', u'part', u'biography', u'part', u'feature', u'film', u'remember', u'going', u'see', u'cinema', u'originally', u'released', u'subtle', u'messages', u'mj', u'feeling', u'towards', u'press', u'also', u'obvious', u'message', u'drugs', u'bad', u'm', u'kay',.....]\n```\n\n不要担心在每个单词之前的`u`；它只是表明 Python 在内部将每个单词表示为 [unicode 字符串](https://docs.python.org/2/howto/unicode.html#python-2-x-s-unicode-support)。\n\n我们可以对数据做很多其他的事情 - 例如，Porter Stemming（词干提取）和 Lemmatizing（词形还原）（都在 NLTK 中提供）将允许我们将`\"messages\"`，`\"message\"`和`\"messaging\"`视为同一个词，这当然可能很有用。 但是，为简单起见，本教程将就此打住。\n\n### 把它们放在一起\n\n现在我们有了清理评论的代码 - 但我们需要清理 25,000 个训练评论！ 为了使我们的代码可重用，让我们创建一个可以多次调用的函数：\n\n```py\ndef review_to_words( raw_review ):\n    # 将原始评论转换为单词字符串的函数\n    # 输入是单个字符串（原始电影评论），\n    # 输出是单个字符串（预处理过的电影评论）\n    # 1. 移除 HTML\n    review_text = BeautifulSoup(raw_review).get_text() \n    #\n    # 2. 移除非字母        \n    letters_only = re.sub(\"[^a-zA-Z]\", \" \", review_text) \n    #\n    # 3. 转换为小写，分成单个单词\n    words = letters_only.lower().split()                             \n    #\n    # 4. 在Python中，搜索集合比搜索列表快得多，\n    #    所以将停止词转换为一个集合\n    stops = set(stopwords.words(\"english\"))                  \n    # \n    # 5. 删除停止词\n    meaningful_words = [w for w in words if not w in stops]   \n    #\n    # 6. 将单词连接成由空格分隔的字符串，\n    #    并返回结果。\n    return( \" \".join( meaningful_words ))   \n```\n\n这里有两个新元素：首先，我们将停止词列表转换为不同的数据类型，即集合。 这是为了速度；因为我们将调用这个函数数万次，所以它需要很快，而 Python 中的搜索集合比搜索列表要快得多。\n\n其次，我们将这些单词合并为一段。 这是为了使输出更容易在我们的词袋中使用，在下面。 定义上述函数后，如果你为单个评论调用该函数：\n\n```py\nclean_review = review_to_words( train[\"review\"][0] )\nprint clean_review\n```\n\n它应该为你提供与前面教程部分中所做的所有单独步骤完全相同的输出。 现在让我们遍历并立即清理所有训练集（这可能需要几分钟，具体取决于你的计算机）：\n\n```py\n# 根据 dataframe 列大小获取评论数\nnum_reviews = train[\"review\"].size\n\n# 初始化空列表来保存清理后的评论\nclean_train_reviews = []\n\n# 遍历每个评论；创建索引 i\n# 范围是 0 到电影评论列表长度\nfor i in xrange( 0, num_reviews ):\n    # 为每个评论调用我们的函数，\n    # 并将结果添加到清理后评论列表中\n    clean_train_reviews.append( review_to_words( train[\"review\"][i] ) )\n```\n\n有时等待冗长的代码的运行会很烦人。 编写提供状态更新的代码会很有帮助。 要让 Python 在其处理每 1000 个评论后打印状态更新，请尝试在上面的代码中添加一两行：\n\n```py\nprint \"Cleaning and parsing the training set movie reviews...\\n\"\nclean_train_reviews = []\nfor i in xrange( 0, num_reviews ):\n    # 如果索引被 1000 整除，打印消息\n    if( (i+1)%1000 == 0 ):\n        print \"Review %d of %d\\n\" % ( i+1, num_reviews )                                                                    \n    clean_train_reviews.append( review_to_words( train[\"review\"][i] ))\n```\n\n### 从词袋创建特征（使用`sklearn`）\n\n现在我们已经整理了我们的训练评论，我们如何将它们转换为机器学习的某种数字表示？一种常见的方法叫做词袋。词袋模型从所有文档中学习词汇表，然后通过计算每个单词出现的次数对每个文档进行建模。例如，考虑以下两句话：\n\n句子1：`\"The cat sat on the hat\"`\n\n句子2：`\"The dog ate the cat and the hat\"`\n\n从这两个句子中，我们的词汇如下：\n\n`{ the, cat, sat, on, hat, dog, ate, and }`\n\n为了得到我们的词袋，我们计算每个单词出现在每个句子中的次数。在句子 1 中，“the”出现两次，“cat”，“sat”，“on”和“hat”每次出现一次，因此句子 1 的特征向量是：\n\n`{ the, cat, sat, on, hat, dog, ate, and }`\n\n句子 1：`{ 2, 1, 1, 1, 1, 0, 0, 0 }`\n\n同样，句子 2 的特征是：`{ 3, 1, 0, 0, 1, 1, 1, 1}`\n\n在 IMDB 数据中，我们有大量的评论，这将为我们提供大量的词汇。要限制特征向量的大小，我们应该选择最大词汇量。下面，我们使用 5000 个最常用的单词（记住已经删除了停止词）。\n\n我们将使用 scikit-learn 中的`feature_extraction`模块来创建词袋特征。如果你学习了泰坦尼克号竞赛中的随机森林教程，那么你应该已经安装了 scikit-learn；否则你需要[安装它](http://scikit-learn.org/stable/install.html)。\n\n```py\nprint \"Creating the bag of words...\\n\"\nfrom sklearn.feature_extraction.text import CountVectorizer\n\n# 初始化 \"CountVectorizer\" 对象，\n# 这是 scikit-learn 的一个词袋工具。\nvectorizer = CountVectorizer(analyzer = \"word\",   \\\n                             tokenizer = None,    \\\n                             preprocessor = None, \\\n                             stop_words = None,   \\\n                             max_features = 5000) \n\n# fit_transform() 有两个功能：\n# 首先，它拟合模型并学习词汇；\n# 第二，它将我们的训练数据转换为特征向量。\n# fit_transform 的输入应该是字符串列表。\ntrain_data_features = vectorizer.fit_transform(clean_train_reviews)\n\n# Numpy 数组很容易使用，因此将结果转换为数组\ntrain_data_features = train_data_features.toarray()\n```\n\n要查看训练数据数组现在的样子，请执行以下操作：\n\n```py\n>>> print train_data_features.shape\n(25000, 5000)\n```\n\n它有 25,000 行和 5,000 个特征（每个词汇一个）。\n\n请注意，`CountVectorizer`有自己的选项来自动执行预处理，标记化和停止词删除 - 对于其中的每一个，我们不指定`None`，可以使用内置方法或指定我们自己的函数来使用。 详细信息请参阅[函数文档](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)。 但是，我们想在本教程中编写我们自己的数据清理函数，来向你展示如何逐步完成它。\n\n现在词袋模型已经训练好了，让我们来看看词汇表：\n\n```py\n# 看看词汇表中的单词\nvocab = vectorizer.get_feature_names()\nprint vocab\n```\n\n如果你有兴趣，还可以打印词汇表中每个单词的计数：\n\n```py\nimport numpy as np\n\n# 求和词汇表中每个单词的计数\ndist = np.sum(train_data_features, axis=0)\n\n# 对于每个词，打印它和它在训练集中的出现次数\nfor tag, count in zip(vocab, dist):\n    print count, tag\n```\n\n### 随机森林\n\n到了这里，我们有词袋的数字训练特征和每个特征向量的原始情感标签，所以让我们做一些监督学习！ 在这里，我们将使用我们在泰坦尼克号教程中介绍的随机森林分类器。 随机森林算法包含在 scikit-learn 中（[随机森林](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)使用许多基于树的分类器来进行预测，因此是“森林”）。 下面，我们将树的数量设置为 100 作为合理的默认值。 更多树可能（或可能不）表现更好，但肯定需要更长时间来运行。 同样，每个评论所包含的特征越多，所需的时间就越长。\n\n```py\nprint \"Training the random forest...\"\nfrom sklearn.ensemble import RandomForestClassifier\n\n# 使用 100 棵树初始化随机森林分类器\nforest = RandomForestClassifier(n_estimators = 100) \n\n# 使用词袋作为特征并将情感标签作为响应变量，使森林拟合训练集\n# 这可能需要几分钟来运行\nforest = forest.fit( train_data_features, train[\"sentiment\"] )\n```\n\n### 创建提交\n\n剩下的就是在我们的测试集上运行训练好的随机森林并创建一个提交文件。 如果你还没有这样做，请从“数据”页面下载`testData.tsv`。 此文件包含另外 25,000 条评论和标签；我们的任务是预测情感标签。\n\n请注意，当我们使用词袋作为测试集时，我们只调用`transform`，而不是像训练集那样调用`fit_transform`。 在机器学习中，你不应该使用测试集来拟合你的模型，否则你将面临[过拟合](http://blog.kaggle.com/2012/07/06/the-dangers-of-overfitting-psychopathy-post-mortem/)的风险。 出于这个原因，我们将测试集保持在禁止状态，直到我们准备好进行预测。\n\n```py\n# 读取测试数据\ntest = pd.read_csv(\"testData.tsv\", header=0, delimiter=\"\\t\", \\\n                   quoting=3 )\n\n# 验证有 25,000 行和 2 列\nprint test.shape\n\n# 创建一个空列表并逐个附加干净的评论\nnum_reviews = len(test[\"review\"])\nclean_test_reviews = [] \n\nprint \"Cleaning and parsing the test set movie reviews...\\n\"\nfor i in xrange(0,num_reviews):\n    if( (i+1) % 1000 == 0 ):\n        print \"Review %d of %d\\n\" % (i+1, num_reviews)\n    clean_review = review_to_words( test[\"review\"][i] )\n    clean_test_reviews.append( clean_review )\n\n# 获取测试集的词袋，并转换为 numpy 数组\ntest_data_features = vectorizer.transform(clean_test_reviews)\ntest_data_features = test_data_features.toarray()\n\n# 使用随机森林进行情感标签预测\nresult = forest.predict(test_data_features)\n\n# 将结果复制到带有 \"id\" 列和 \"sentiment\" 列的 pandas dataframe\noutput = pd.DataFrame( data={\"id\":test[\"id\"], \"sentiment\":result} )\n\n# 使用 pandas 编写逗号分隔的输出文件\noutput.to_csv( \"Bag_of_Words_model.csv\", index=False, quoting=3 )\n```\n\n恭喜，你已准备好第一次提交！ 尝试不同的事情，看看你的结果如何变化。 你可以以不同方式清理评论，为词袋表示选择不同数量的词汇表单词，尝试 Porter Stemming，不同的分类器或任何其他的东西。 要在不同的数据集上试用你的 NLP 招式，你还可以参加我们的[烂番茄比赛](https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews)。 或者，如果你为完全不同的东西做好了准备，请访问深度学习和词向量页面。\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/官方教程翻译/2.md",
    "content": "## 第二部分：词向量\n\n### 代码\n\n第二部分的教程代码[在这里](https://github.com/wendykan/DeepLearningMovies/blob/master/Word2Vec_AverageVectors.py)。\n\n### 分布式词向量简介\n\n本教程的这一部分将重点介绍使用 Word2Vec 算法创建分布式单词向量。 （深度学习的概述，以及其他一些教程的链接，请参阅“什么是深度学习？”页面）。\n\n第 2 部分和第 3 部分比第 1 部分假设你更熟悉Python。我们在双核 Macbook Pro 上开发了以下代码，但是，我们还没有在 Windows 上成功运行代码。如果你是 Windows 用户并且使其正常运行，请在论坛中留言如何进行操作！更多详细信息，请参阅“配置系统”页面。\n\n[Word2vec](https://code.google.com/p/word2vec/)，由 Google 于 2013 年发表，是一种神经网络实现，可以学习单词的[分布式表示](http://www.cs.toronto.edu/~bonner/courses/2014s/csc321/lectures/lec5.pdf)。在此之前已经提出了用于学习单词表示的其他深度或循环神经网络架构，但是这些的主要问题是训练模型所需时长间。 Word2vec 相对于其他模型学习得快。\n\nWord2Vec 不需要标签来创建有意义的表示。这很有用，因为现实世界中的大多数数据都是未标记的。如果给网络足够的训练数据（数百亿个单词），它会产生特征极好的单词向量。具有相似含义的词出现在簇中，并且簇具有间隔，使得可以使用向量数学来再现诸如类比的一些词关系。着名的例子是，通过训练好的单词向量，“国王 - 男人 + 女人 = 女王”。\n\n查看 [Google 的代码，文章和附带的论文](https://code.google.com/p/word2vec/)。 [此演示](https://docs.google.com/file/d/0B7XkCwpI5KDYRWRnd1RzWXQ2TWc/edit)也很有帮助。 原始代码是 C 写的，但它已被移植到其他语言，包括 Python。 我们鼓励你使用原始 C 工具，但如果你是初学程序员（我们必须手动编辑头文件来编译），请注意它不是用户友好的。\n\n最近斯坦福大学的工作也将[深度学习应用于情感分析](http://nlp.stanford.edu/sentiment/)；他们的代码以 Java 提供。 但是，他们的方法依赖于句子解析，不能直接应用于任意长度的段落。\n\n分布式词向量强大，可用于许多应用，尤其是单词预测和转换。 在这里，我们将尝试将它们应用于情感分析。\n\n### 在 Python 中使用 word2vec\n\n在 Python 中，我们将使用`gensim`包中的 word2vec 的优秀实现。 如果你还没有安装`gensim`，则需要[安装](http://radimrehurek.com/gensim/install.html)它。 [这里](http://radimrehurek.com/2014/02/word2vec-tutorial/)有一个包含 Python Word2Vec 实现的优秀教程。\n\n虽然 Word2Vec 不像许多深度学习算法那样需要图形处理单元（GPU），但它是计算密集型的。 Google 的版本和 Python 版本都依赖于多线程（在你的计算机上并行运行多个进程以节省时间）。 为了在合理的时间内训练你的模型，你需要安装 cython（[这里是指南](http://docs.cython.org/src/quickstart/install.html)）。 Word2Vec 可在没有安装 cython 的情况下运行，但运行它需要几天而不是几分钟。\n\n### 为训练模型做准备\n\n现在到了细节！ 首先，我们使用`pandas`读取数据，就像我们在第 1 部分中所做的那样。与第 1 部分不同，我们现在使用`unlabeledTrain.tsv`，其中包含 50,000 个额外的评论，没有标签。 当我们在第 1 部分中构建词袋模型时，额外的未标记的训练评论没有用。 但是，由于 Word2Vec 可以从未标记的数据中学习，现在可以使用这些额外的 50,000 条评论。\n\n```py\nimport pandas as pd\n\n# 从文件读取数据\ntrain = pd.read_csv( \"labeledTrainData.tsv\", header=0, \n delimiter=\"\\t\", quoting=3 )\ntest = pd.read_csv( \"testData.tsv\", header=0, delimiter=\"\\t\", quoting=3 )\nunlabeled_train = pd.read_csv( \"unlabeledTrainData.tsv\", header=0, \n delimiter=\"\\t\", quoting=3 )\n\n# 验证已读取的评论数量（总共 100,000 个）\nprint \"Read %d labeled train reviews, %d labeled test reviews, \" \\\n \"and %d unlabeled reviews\\n\" % (train[\"review\"].size,  \n test[\"review\"].size, unlabeled_train[\"review\"].size )\n```\n\n我们为清理数据而编写的函数也与第 1 部分类似，尽管现在存在一些差异。 首先，为了训练 Word2Vec，最好不要删除停止词，因为算法依赖于句子的更广泛的上下文，以便产生高质量的词向量。 因此，我们将在下面的函数中，将停止词删除变成可选的。 最好不要删除数字，但我们将其留作读者的练习。\n\n```py\n# Import various modules for string cleaning\nfrom bs4 import BeautifulSoup\nimport re\nfrom nltk.corpus import stopwords\n\ndef review_to_wordlist( review, remove_stopwords=False ):\n    # 将文档转换为单词序列的函数，可选地删除停止词。 返回单词列表。\n    #\n    # 1. 移除 HTML\n    review_text = BeautifulSoup(review).get_text()\n    #  \n    # 2. 移除非字母\n    review_text = re.sub(\"[^a-zA-Z]\",\" \", review_text)\n    #\n    # 3. 将单词转换为小写并将其拆分\n    words = review_text.lower().split()\n    #\n    # 4. 可选地删除停止词（默认为 false）\n    if remove_stopwords:\n        stops = set(stopwords.words(\"english\"))\n        words = [w for w in words if not w in stops]\n    #\n    # 5. 返回单词列表\n    return(words)\n```\n\n接下来，我们需要一种特定的输入格式。 Word2Vec 需要单个句子，每个句子都是一列单词。 换句话说，输入格式是列表的列表。\n\n如何将一个段落分成句子并不简单。 自然语言中有各种各样的问题。 英语句子可能以“?”，“!”，“\"”或“.”等结尾，并且间距和大写也不是可靠的标志。因此，我们将使用 NLTK 的`punkt`分词器进行句子分割。为了使用它，你需要安装 NLTK 并使用`nltk.download()`下载`punkt`的相关训练文件。\n\n```py\n# 为句子拆分下载 punkt 分词器\nimport nltk.data\nnltk.download()   \n\n# 加载 punkt 分词器\ntokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n\n# 定义一个函数将评论拆分为已解析的句子\ndef review_to_sentences( review, tokenizer, remove_stopwords=False ):\n    # 将评论拆分为已解析句子的函数。\n    # 返回句子列表，其中每个句子都是单词列表\n    # 1. 使用 NLTK 分词器将段落拆分为句子\n    raw_sentences = tokenizer.tokenize(review.strip())\n    #\n    # 2. 遍历每个句子\n    sentences = []\n    for raw_sentence in raw_sentences:\n        # 如果句子为空，则跳过\n        if len(raw_sentence) > 0:\n            # 否则，调用 review_to_wordlist 来获取单词列表\n            sentences.append( review_to_wordlist( raw_sentence, \\\n              remove_stopwords ))\n    # 返回句子列表（每个句子都是单词列表，\n    # 因此返回列表的列表）\n    return sentences\n```\n\n现在我们可以应用此函数，来准备 Word2Vec 的输入数据（这将需要几分钟）：\n\n```py\nsentences = []  # 初始化空的句子列表\n\nprint \"Parsing sentences from training set\"\nfor review in train[\"review\"]:\n    sentences += review_to_sentences(review, tokenizer)\n\nprint \"Parsing sentences from unlabeled set\"\nfor review in unlabeled_train[\"review\"]:\n    sentences += review_to_sentences(review, tokenizer)\n```\n\n你可能会从`BeautifulSoup`那里得到一些关于句子中 URL 的警告。 这些都不用担心（尽管你可能需要考虑在清理文本时删除 URL）。\n\n我们可以看一下输出，看看它与第 1 部分的不同之处：\n\n```py\n>>> # 检查我们总共有多少句子 - 应该是 850,000+ 左右\n... print len(sentences)\n857234\n\n>>> print sentences[0]\n[u'with', u'all', u'this', u'stuff', u'going', u'down', u'at', u'the', u'moment', u'with', u'mj', u'i', u've', u'started', u'listening', u'to', u'his', u'music', u'watching', u'the', u'odd', u'documentary', u'here', u'and', u'there', u'watched', u'the', u'wiz', u'and', u'watched', u'moonwalker', u'again']\n\n>>> print sentences[1]\n[u'maybe', u'i', u'just', u'want', u'to', u'get', u'a', u'certain', u'insight', u'into', u'this', u'guy', u'who', u'i', u'thought', u'was', u'really', u'cool', u'in', u'the', u'eighties', u'just', u'to', u'maybe', u'make', u'up', u'my', u'mind', u'whether', u'he', u'is', u'guilty', u'or', u'innocent']\n```\n\n需要注意的一个小细节是 Python 列表中`+=`和`append`之间的区别。 在许多应用中，这两者是可以互换的，但在这里它们不是。 如果要将列表列表附加到另一个列表列表，`append`仅仅附加外层列表; 你需要使用`+=`才能连接所有内层列表。\n\n> 译者注：原文中这里的解释有误，已修改。\n\n### 训练并保存你的模型\n\n使用精心解析的句子列表，我们已准备好训练模型。 有许多参数选项会影响运行时间和生成的最终模型的质量。 以下算法的详细信息，请参阅 [word2vec API 文档](http://radimrehurek.com/gensim/models/word2vec.html)以及 [Google 文档](https://code.google.com/p/word2vec/)。\n\n+   架构：架构选项是 skip-gram（默认）或 CBOW。 我们发现 skip-gram 非常慢，但产生了更好的结果。\n+   训练算法：分层 softmax（默认）或负采样。 对我们来说，默认效果很好。\n+   对频繁词汇进行下采样：Google 文档建议值介于`.00001`和`.001`之间。 对我们来说，接近0.001的值似乎可以提高最终模型的准确性。\n+   单词向量维度：更多特征会产生更长的运行时间，并且通常（但并非总是）会产生更好的模型。 合理的值可能介于几十到几百；我们用了 300。\n+   上下文/窗口大小：训练算法应考虑多少个上下文单词？ 10 似乎适用于分层 softmax（越多越好，达到一定程度）。\n+   工作线程：要运行的并行进程数。 这是特定于计算机的，但 4 到 6 之间应该适用于大多数系统。\n+   最小词数：这有助于将词汇量的大小限制为有意义的单词。 在所有文档中，至少没有出现这个次数的任何单词都将被忽略。 合理的值可以在 10 到 100 之间。在这种情况下，由于每个电影出现 30 次，我们将最小字数设置为 40，来避免过分重视单个电影标题。 这导致了整体词汇量大约为 15,000 个单词。 较高的值也有助于限制运行时间。\n\n选择参数并不容易，但是一旦我们选择了参数，创建 Word2Vec 模型就很简单：\n\n```py\n# 导入内置日志记录模块并配置它，以便 Word2Vec 创建良好的输出消息\nimport logging\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',\\\n    level=logging.INFO)\n\n# 设置各种参数的值\nnum_features = 300    # 词向量维度\nmin_word_count = 40   # 最小单词数\nnum_workers = 4       # 并行运行的线程数\ncontext = 10          # 上下文窗口大小\ndownsampling = 1e-3   # 为频繁词设置下采样\n\n# 初始化并训练模型（这需要一些时间）\nfrom gensim.models import word2vec\nprint \"Training model...\"\nmodel = word2vec.Word2Vec(sentences, workers=num_workers, \\\n            size=num_features, min_count = min_word_count, \\\n            window = context, sample = downsampling)\n\n# 如果你不打算再进一步训练模型，\n# 则调用 init_sims 将使模型更具内存效率。\nmodel.init_sims(replace=True)\n\n# 创建有意义的模型名称并保存模型以供以后使用会很有帮助。 \n# 你可以稍后使用 Word2Vec.load() 加载它\nmodel_name = \"300features_40minwords_10context\"\nmodel.save(model_name)\n```\n\n在双核 Macbook Pro 上，使用 4 个工作线程来运行，花费不到 15 分钟。 但是，它会因你的计算机而异。 幸运的是，日志记录功能可以打印带有信息的消息。\n\n如果你使用的是 Mac 或 Linux 系统，则可以使用终端内（而不是来自 Python 内部）的`top`命令，来查看你的系统是否在模型训练时成功并行化。 键入：\n\n```\n> top -o cpu \n```\n\n在模型训练时进入终端窗口。 对于 4 个 worker，列表中的第一个进程应该是 Python，它应该显示 300-400% 的 CPU 使用率。\n\n![](https://kaggle2.blob.core.windows.net/competitions/kaggle/3971/media/Screen%20Shot%202014-08-04%20at%202.01.40%20PM.png)\n\n如果你的 CPU 使用率较低，则可能是你的计算机上的 cython 无法正常运行。\n\n### 探索模型结果\n\n恭喜你到目前为止成功通过了一切！ 让我们来看看我们在 75,000 个训练评论中创建的模型。\n\n`doesnt_match`函数将尝试推断集合中哪个单词与其他单词最不相似：\n\n```py\n>>> model.doesnt_match(\"man woman child kitchen\".split())\n'kitchen'\n```\n\n我们的模型能够区分意义上的差异！ 它知道男人，女人和孩子彼此更相似，而不是厨房。 更多的探索表明，该模型对意义上更微妙的差异敏感，例如国家和城市之间的差异：\n\n```py\n>>> model.doesnt_match(\"france england germany berlin\".split())\n'berlin'\n```\n\n...虽然我们使用的训练集相对较小，但肯定不完美：\n\n```py\n>>> model.doesnt_match(\"paris berlin london austria\".split())\n'paris'\n```\n\n我们还可以使用`most_similar`函数来深入了解模型的单词簇：\n\n```py\n>>> model.most_similar(\"man\")\n[(u'woman', 0.6056041121482849), (u'guy', 0.4935004413127899), (u'boy', 0.48933547735214233), (u'men', 0.4632953703403473), (u'person', 0.45742249488830566), (u'lady', 0.4487500488758087), (u'himself', 0.4288588762283325), (u'girl', 0.4166809320449829), (u'his', 0.3853422999382019), (u'he', 0.38293731212615967)]\n\n>>> model.most_similar(\"queen\")\n[(u'princess', 0.519856333732605), (u'latifah', 0.47644317150115967), (u'prince', 0.45914226770401), (u'king', 0.4466976821422577), (u'elizabeth', 0.4134873151779175), (u'antoinette', 0.41033703088760376), (u'marie', 0.4061327874660492), (u'stepmother', 0.4040161967277527), (u'belle', 0.38827288150787354), (u'lovely', 0.38668593764305115)]\n```\n\n鉴于我们特定的训练集，“Latifah”与“女王”的相似性最高，也就不足为奇了。\n\n或者，与情感分析更相关：\n\n```py\n>>> model.most_similar(\"awful\")\n[(u'terrible', 0.6812670230865479), (u'horrible', 0.62867271900177), (u'dreadful', 0.5879652500152588), (u'laughable', 0.5469599962234497), (u'horrendous', 0.5167273283004761), (u'atrocious', 0.5115568041801453), (u'ridiculous', 0.5104714632034302), (u'abysmal', 0.5015234351158142), (u'pathetic', 0.4880446791648865), (u'embarrassing', 0.48272213339805603)]\n```\n\n因此，似乎我们有相当好的语义意义模型 - 至少和词袋一样好。 但是，我们如何才能将这些花哨的分布式单词向量用于监督学习呢？ 下一节将对此进行一次尝试。\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/官方教程翻译/3.md",
    "content": "## 第三部分：词向量的更多乐趣\n\n### 代码\n\n第三部分的代码在[这里](https://github.com/wendykan/DeepLearningMovies/blob/master/Word2Vec_BagOfCentroids.py)。\n\n### 单词的数值表示\n\n现在我们有了训练好的模型，对单词有一些语义理解，我们应该如何使用它？ 如果你看它的背后，第 2 部分训练的 Word2Vec 模型由词汇表中每个单词的特征向量组成，存储在一个名为`syn0`的[`numpy`](http://www.numpy.org/)数组中：\n\n```py\n>>> # Load the model that we created in Part 2\n>>> from gensim.models import Word2Vec\n>>> model = Word2Vec.load(\"300features_40minwords_10context\")\n2014-08-03 14:50:15,126 : INFO : loading Word2Vec object from 300features_40min_word_count_10context\n2014-08-03 14:50:15,777 : INFO : setting ignored attribute syn0norm to None\n\n>>> type(model.syn0)\n<type 'numpy.ndarray'>\n\n>>> model.syn0.shape\n(16492, 300)\n```\n\n`syn0`中的行数是模型词汇表中的单词数，列数对应于我们在第 2 部分中设置的特征向量的大小。将最小单词计数设置为 40 ，总词汇量为 16,492 个单词，每个词有 300 个特征。 可以通过以下方式访问单个单词向量：\n\n```py\n>>> model[\"flower\"]\n```\n\n...返回一个 1x300 的`numpy`数组。\n\n### 从单词到段落，尝试 1：向量平均\n\nIMDB 数据集的一个挑战是可变长度评论。 我们需要找到一种方法来获取单个单词向量并将它们转换为每个评论的长度相同的特征集。\n\n由于每个单词都是 300 维空间中的向量，我们可以使用向量运算来组合每个评论中的单词。 我们尝试的一种方法是简单地平均给定的评论中的单词向量（为此，我们删除了停止词，这只会增加噪音）。\n\n以下代码基于第 2 部分的代码构建了特征向量的平均值。\n\n```py\nimport numpy as np  # Make sure that numpy is imported\n\ndef makeFeatureVec(words, model, num_features):\n    # 用于平均给定段落中的所有单词向量的函数\n    #\n    # 预初始化一个空的 numpy 数组（为了速度）\n    featureVec = np.zeros((num_features,),dtype=\"float32\")\n    #\n    nwords = 0.\n    # \n    # Index2word 是一个列表，包含模型词汇表中的单词名称。\n    # 为了获得速度，将其转换为集合。 \n    index2word_set = set(model.index2word)\n    #\n    # 遍历评论中的每个单词，如果它在模型的词汇表中，\n    # 则将其特征向量加到 total\n    for word in words:\n        if word in index2word_set: \n            nwords = nwords + 1.\n            featureVec = np.add(featureVec,model[word])\n    # \n    # 将结果除以单词数来获得平均值\n    featureVec = np.divide(featureVec,nwords)\n    return featureVec\n\n\ndef getAvgFeatureVecs(reviews, model, num_features):\n    # 给定一组评论（每个评论都是单词列表），计算每个评论的平均特征向量并返回2D numpy数组\n    # \n    # 初始化计数器\n    counter = 0.\n    # \n    # 为了速度，预分配 2D numpy 数组\n    reviewFeatureVecs = np.zeros((len(reviews),num_features),dtype=\"float32\")\n    # \n    # 遍历评论\n    for review in reviews:\n       #\n       # 每 1000 个评论打印一次状态消息\n       if counter%1000. == 0.:\n           print \"Review %d of %d\" % (counter, len(reviews))\n       # \n       # 调用生成平均特征向量的函数（定义如上）\n       reviewFeatureVecs[counter] = makeFeatureVec(review, model, \\\n           num_features)\n       #\n       # 增加计数器\n       counter = counter + 1.\n    return reviewFeatureVecs\n```\n\n现在，我们可以调用这些函数来为每个段落创建平均向量。 以下操作将需要几分钟：\n\n```py\n# ****************************************************************\n# 使用我们在上面定义的函数，\n# 计算训练和测试集的平均特征向量。\n# 请注意，我们现在删除停止词。\n\nclean_train_reviews = []\nfor review in train[\"review\"]:\n    clean_train_reviews.append( review_to_wordlist( review, \\\n        remove_stopwords=True ))\n\ntrainDataVecs = getAvgFeatureVecs( clean_train_reviews, model, num_features )\n\nprint \"Creating average feature vecs for test reviews\"\nclean_test_reviews = []\nfor review in test[\"review\"]:\n    clean_test_reviews.append( review_to_wordlist( review, \\\n        remove_stopwords=True ))\n\ntestDataVecs = getAvgFeatureVecs( clean_test_reviews, model, num_features )\n```\n\n接下来，使用平均段落向量来训练随机森林。 请注意，与第 1 部分一样，我们只能使用标记的训练评论来训练模型。\n\n```py\n# 使用 100 棵树让随机森林拟合训练数据\nfrom sklearn.ensemble import RandomForestClassifier\nforest = RandomForestClassifier( n_estimators = 100 )\n\nprint \"Fitting a random forest to labeled training data...\"\nforest = forest.fit( trainDataVecs, train[\"sentiment\"] )\n\n# 测试和提取结果\nresult = forest.predict( testDataVecs )\n\n# 写出测试结果\noutput = pd.DataFrame( data={\"id\":test[\"id\"], \"sentiment\":result} )\noutput.to_csv( \"Word2Vec_AverageVectors.csv\", index=False, quoting=3 )\n```\n\n我们发现这产生了比偶然更好的结果，但是表现比词袋低了几个百分点。\n\n由于向量的元素平均值没有产生惊人的结果，或许我们可以以更聪明的方式实现？ 加权单词向量的标准方法是应用[“tf-idf”](http://en.wikipedia.org/wiki/Tf%E2%80%93idf)权重，它衡量给定单词在给定文档集中的重要程度。 在 Python 中提取 tf-idf 权重的一种方法，是使用 scikit-learn 的[`TfidfVectorizer`](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html)，它具有类似于我们在第 1 部分中使用的`CountVectorizer`的接口。但是，当我们尝试以这种方式加权我们的单词向量时，我们发现没有实质的性能改善。\n\n### 从单词到段落，尝试 2：聚类\n\nWord2Vec 创建语义相关单词的簇，因此另一种可能的方法是利用簇中单词的相似性。 以这种方式来分组向量称为“向量量化”。 为了实现它，我们首先需要找到单词簇的中心，我们可以通过使用[聚类算法](http://scikit-learn.org/stable/modules/clustering.html)（如 [K-Means](http://en.wikipedia.org/wiki/K-means_clustering)）来完成。\n\n在 K-Means 中，我们需要设置的一个参数是“K”，或者是簇的数量。 我们应该如何决定要创建多少个簇？ 试错法表明，每个簇平均只有5个单词左右的小簇，比具有多个词的大簇产生更好的结果。 聚类代码如下。 我们使用 [scikit-learn 来执行我们的 K-Means](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)。\n\n具有较大 K 的 K-Means 聚类可能非常慢；以下代码在我的计算机上花了 40 多分钟。 下面，我们给 K-Means 函数设置一个计时器，看看它需要多长时间。\n\n```py\nfrom sklearn.cluster import KMeans\nimport time\n\nstart = time.time() # Start time\n\n# 将“k”（num_clusters）设置为词汇量大小的 1/5，或每个簇平均 5 个单词\nword_vectors = model.syn0\nnum_clusters = word_vectors.shape[0] / 5\n\n# 初始化 k-means 对象并使用它来提取质心\nkmeans_clustering = KMeans( n_clusters = num_clusters )\nidx = kmeans_clustering.fit_predict( word_vectors )\n\n# 获取结束时间并打印该过程所需的时间\nend = time.time()\nelapsed = end - start\nprint \"Time taken for K Means clustering: \", elapsed, \"seconds.\"\n```\n\n现在，每个单词的聚类分布都存储在`idx`中，而原始 Word2Vec 模型中的词汇表仍存储在`model.index2word`中。 为方便起见，我们将它们压缩成一个字典，如下所示：\n\n```py\n# 创建单词/下标字典，将每个词汇表单词映射为簇编号\nword_centroid_map = dict(zip( model.index2word, idx ))\n```\n\n这有点抽象，所以让我们仔细看看我们的簇包含什么。 你的簇可能会有所不同，因为 Word2Vec 依赖于随机数种子。 这是一个循环，打印出簇 0 到 9 的单词：\n\n```py\n# 对于前 10 个簇\nfor cluster in xrange(0,10):\n    #\n    # 打印簇编号\n    print \"\\nCluster %d\" % cluster\n    #\n    # 找到该簇编号的所有单词，然后将其打印出来\n    words = []\n    for i in xrange(0,len(word_centroid_map.values())):\n        if( word_centroid_map.values()[i] == cluster ):\n            words.append(word_centroid_map.keys()[i])\n    print words\n```\n\n结果很有意思：\n\n```py\nCluster 0\n[u'passport', u'penthouse', u'suite', u'seattle', u'apple']\n\nCluster 1\n[u'unnoticed']\n\nCluster 2\n[u'midst', u'forming', u'forefront', u'feud', u'bonds', u'merge', u'collide', u'dispute', u'rivalry', u'hostile', u'torn', u'advancing', u'aftermath', u'clans', u'ongoing', u'paths', u'opposing', u'sexes', u'factions', u'journeys']\n\nCluster 3\n[u'lori', u'denholm', u'sheffer', u'howell', u'elton', u'gladys', u'menjou', u'caroline', u'polly', u'isabella', u'rossi', u'nora', u'bailey', u'mackenzie', u'bobbie', u'kathleen', u'bianca', u'jacqueline', u'reid', u'joyce', u'bennett', u'fay', u'alexis', u'jayne', u'roland', u'davenport', u'linden', u'trevor', u'seymour', u'craig', u'windsor', u'fletcher', u'barrie', u'deborah', u'hayward', u'samantha', u'debra', u'frances', u'hildy', u'rhonda', u'archer', u'lesley', u'dolores', u'elsie', u'harper', u'carlson', u'ella', u'preston', u'allison', u'sutton', u'yvonne', u'jo', u'bellamy', u'conte', u'stella', u'edmund', u'cuthbert', u'maude', u'ellen', u'hilary', u'phyllis', u'wray', u'darren', u'morton', u'withers', u'bain', u'keller', u'martha', u'henderson', u'madeline', u'kay', u'lacey', u'topper', u'wilding', u'jessie', u'theresa', u'auteuil', u'dane', u'jeanne', u'kathryn', u'bentley', u'valerie', u'suzanne', u'abigail']\n\nCluster 4\n[u'fest', u'flick']\n\nCluster 5\n[u'lobster', u'deer']\n\nCluster 6\n[u'humorless', u'dopey', u'limp']\n\nCluster 7\n[u'enlightening', u'truthful']\n\nCluster 8\n[u'dominates', u'showcases', u'electrifying', u'powerhouse', u'standout', u'versatility', u'astounding']\n\nCluster 9\n[u'succumbs', u'comatose', u'humiliating', u'temper', u'looses', u'leans']\n```\n\n我们可以看到这些簇的质量各不相同。 有些是有道理的 - 簇 3 主要包含名称，而簇 6- 8包含相关的形容词（簇 6 是我最喜欢的）。 另一方面，簇 5 有点神秘：龙虾和鹿有什么共同之处（除了是两只动物）？ 簇 0 更糟糕：阁楼和套房似乎属于一个东西，但它们似乎不属于苹果和护照。 簇 2 包含......可能与战争有关的词？ 也许我们的算法在形容词上效果最好。\n\n无论如何，现在我们为每个单词分配了一个簇（或“质心”），我们可以定义一个函数将评论转换为质心袋。 这就像词袋一样，但使用语义相关的簇而不是单个单词：\n\n```py\ndef create_bag_of_centroids( wordlist, word_centroid_map ):\n    #\n    # 簇的数量等于单词/质心映射中的最大的簇索引\n    num_centroids = max( word_centroid_map.values() ) + 1\n    #\n    # 预分配质心向量袋（为了速度）\n    bag_of_centroids = np.zeros( num_centroids, dtype=\"float32\" )\n    #\n    # 遍历评论中的单词。如果单词在词汇表中，\n    # 找到它所属的簇，并将该簇的计数增加 1\n    for word in wordlist:\n        if word in word_centroid_map:\n            index = word_centroid_map[word]\n            bag_of_centroids[index] += 1\n    #\n    # 返回“质心袋”\n    return bag_of_centroids\n```\n\n上面的函数将为每个评论提供一个`numpy`数组，每个数组的特征都与簇数相等。 最后，我们为训练和测试集创建了质心袋，然后训练随机森林并提取结果：\n\n```py\n# 为训练集质心预分配一个数组（为了速度）\ntrain_centroids = np.zeros( (train[\"review\"].size, num_clusters), \\\n    dtype=\"float32\" )\n\n# 将训练集评论转换为质心袋\ncounter = 0\nfor review in clean_train_reviews:\n    train_centroids[counter] = create_bag_of_centroids( review, \\\n        word_centroid_map )\n    counter += 1\n\n# 对测试评论重复\ntest_centroids = np.zeros(( test[\"review\"].size, num_clusters), \\\n    dtype=\"float32\" )\n\ncounter = 0\nfor review in clean_test_reviews:\n    test_centroids[counter] = create_bag_of_centroids( review, \\\n        word_centroid_map )\n    counter += 1\n```\n\n```py\n# 拟合随机森林并提取预测\nforest = RandomForestClassifier(n_estimators = 100)\n\n# 拟合可能需要几分钟\nprint \"Fitting a random forest to labeled training data...\"\nforest = forest.fit(train_centroids,train[\"sentiment\"])\nresult = forest.predict(test_centroids)\n\n# 写出测试结果\noutput = pd.DataFrame(data={\"id\":test[\"id\"], \"sentiment\":result})\noutput.to_csv( \"BagOfCentroids.csv\", index=False, quoting=3 )\n```\n\n我们发现与第 1 部分中的词袋相比，上面的代码给出了相同（或略差）的结果。\n\n### 深度和非深度学习方法的比较\n\n你可能会问：为什么词袋更好？\n\n最大的原因是，在我们的教程中，平均向量和使用质心会失去单词的顺序，这使得它与词袋的概念非常相似。性能相似（在标准误差范围内）的事实使得所有三种方法实际上相同。\n\n一些要尝试的事情：\n\n首先，在更多文本上训练 Word2Vec 应该会大大提高性能。谷歌的结果基于从超过十亿字的语料库中学到的单词向量；我们标记和未标记的训练集合在一起只有 1800 万字左右。方便的是，Word2Vec 提供了加载由谷歌原始 C 工具输出的任何预训练模型的函数，因此也可以用 C 训练模型然后将其导入 Python。\n\n其次，在已发表的文献中，分布式单词向量技术已被证明优于词袋模型。在本文中，在 IMDB 数据集上使用了一种名为段落向量的算法，来生成迄今为止最先进的一些结果。在某种程度上，它比我们在这里尝试的方法更好，因为向量平均和聚类会丢失单词顺序，而段落向量会保留单词顺序信息。\n"
  },
  {
    "path": "docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/官方教程翻译/README.md",
    "content": "# Kaggle word2vec NLP 教程\n\n> 原文：[Bag of Words Meets Bags of Popcorn](https://www.kaggle.com/c/word2vec-nlp-tutorial)\n> \n> 译者：[飞龙](https://github.com/wizardforcel)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n> \n> 自豪地采用[谷歌翻译](https://translate.google.cn/)\n"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/baseline.md",
    "content": "# baseline\n\n> Author: https://www.kaggle.com/ivanwang2016\n\n> From: https://www.kaggle.com/ivanwang2016/baseline\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\nIn [1]:\n\n```py\n# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n# Any results you write to the current directory are saved as output.\n\n```\n\n```\n['test', 'sample_submission.csv', 'train.csv', 'train']\n\n```\n\nIn [2]:\n\n```py\nfrom PIL import Image\nfrom tqdm import tqdm\n\nfrom keras.preprocessing.image import ImageDataGenerator\n\ndef load_data(dataframe=None, batch_size=16, mode='categorical'):\n    if dataframe is None:\n        dataframe = pd.read_csv('../input/train.csv')\n    dataframe['has_cactus'] = dataframe['has_cactus'].apply(str)\n    gen = ImageDataGenerator(rescale=1./255., validation_split=0.1, horizontal_flip=True, vertical_flip=True)\n\n    trainGen = gen.flow_from_dataframe(dataframe, directory='../input/train/train', x_col='id', y_col='has_cactus', has_ext=True, target_size=(32, 32),\n        class_mode=mode, batch_size=batch_size, shuffle=True, subset='training')\n    testGen = gen.flow_from_dataframe(dataframe, directory='../input/train/train', x_col='id', y_col='has_cactus', has_ext=True, target_size=(32, 32),\n        class_mode=mode, batch_size=batch_size, shuffle=True, subset='validation')\n\n    return trainGen, testGen\n\n```\n\n```\nUsing TensorFlow backend.\n\n```\n\nIn [3]:\n\n```py\nfrom keras.layers import Conv2D, MaxPool2D, Dense, BatchNormalization, Activation, GlobalAveragePooling2D\nfrom keras.models import Sequential, Model\nfrom keras.regularizers import l2\n\ndef baseline_model():\n    model = Sequential()\n\n    model.add(Conv2D(32, (3, 3), input_shape=(32, 32, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(Conv2D(32, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(Conv2D(32, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(MaxPool2D())\n\n    model.add(Conv2D(64, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(Conv2D(64, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(Conv2D(64, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(MaxPool2D())\n\n    model.add(Conv2D(128, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(Conv2D(128, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(Conv2D(128, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(MaxPool2D())\n\n    model.add(GlobalAveragePooling2D())\n    model.add(Dense(2, activation='softmax'))\n\n    return model\n\n```\n\nIn [4]:\n\n```py\nfrom keras.optimizers import Adam, SGD\nfrom keras.callbacks import CSVLogger, ModelCheckpoint, ReduceLROnPlateau\n\ndef train_baseline():\n    batch_size = 32\n    trainGen, valGen = load_data(batch_size=batch_size)\n    model = baseline_model()\n\n    opt = Adam(1e-3)\n    model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n    cbs = [ReduceLROnPlateau(monitor='loss', factor=0.5, patience=1, min_lr=1e-5, verbose=1)]\n\n    model.fit_generator(trainGen, steps_per_epoch=4922, epochs=3, validation_data=valGen, \n        validation_steps=493, shuffle=True, callbacks=cbs)\n\n    return model\n\ndef predict_baseline(model):\n    testdf = pd.read_csv('../input/sample_submission.csv')\n    pred = np.empty((testdf.shape[0],))\n    for n in tqdm(range(testdf.shape[0])):\n        data = np.array(Image.open('../input/test/test/'+testdf.id[n]))\n        data = data.astype(np.float32) / 255.\n        pred[n] = model.predict(data.reshape((1, 32, 32, 3)))[0][1]\n\n    testdf['has_cactus'] = pred\n    testdf.to_csv('sample_submission.csv', index=False)\n\n```\n\nIn [5]:\n\n```py\nmodel = train_baseline()\npredict_baseline(model)\n\n```\n\n```\nFound 15750 images belonging to 2 classes.\nFound 1750 images belonging to 2 classes.\nEpoch 1/3\n4922/4922 [==============================] - 247s 50ms/step - loss: 0.0674 - acc: 0.9889 - val_loss: 0.0786 - val_acc: 0.9813\nEpoch 2/3\n4922/4922 [==============================] - 230s 47ms/step - loss: 0.0326 - acc: 0.9965 - val_loss: 0.0428 - val_acc: 0.9888\nEpoch 3/3\n1400/4922 [=======>......................] - ETA: 2:35 - loss: 0.0273 - acc: 0.9974\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/cactus-identification-ensemble-transfer-learning.md",
    "content": "# Cactus Identification (Ensemble+Transfer Learning)\n\n> Author: https://www.kaggle.com/harshel7\n\n> From: https://www.kaggle.com/harshel7/cactus-identification-ensemble-transfer-learning\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\n> Score: 0.9970\n\nIn [1]:\n\n```py\n# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport glob\nimport os\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport cv2\nfrom tqdm import tqdm_notebook\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.\n\n```\n\n```\n['test', 'train', 'train.csv', 'sample_submission.csv']\n\n```\n\nIn [2]:\n\n```py\n#read in all our files\ntrain_df = pd.read_csv('../input/train.csv')\ntrain_images = '../input/train/*'\ntest_images = '../input/test/*'\n\n```\n\nIn [3]:\n\n```py\ntrain_df.head()\n\n```\n\nOut[3]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |\n| --- | --- | --- |\n| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |\n| --- | --- | --- |\n| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |\n| --- | --- | --- |\n| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |\n| --- | --- | --- |\n| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |\n| --- | --- | --- |\n\nIn [4]:\n\n```py\nsns.set(style = 'darkgrid')\nplt.figure(figsize = (12,10))\nsns.countplot(train_df['has_cactus'])\n\n```\n\nOut[4]:\n\n```\n<matplotlib.axes._subplots.AxesSubplot at 0x7f98563abac8>\n```\n\n![](cactus-identification-ensemble-transfer-learning_files/__results___3_1.png)In [5]:\n\n```py\n#let's visualize some cactus images\nIMAGES = os.path.join(train_images, \"*\")\nall_images = glob.glob(IMAGES)\n\n```\n\nIn [6]:\n\n```py\n#visualize some images\n\nplt.figure(figsize = (12,10))\nplt.subplot(1, 3, 1)\nplt.imshow(plt.imread(all_images[0]))\nplt.xticks([])\nplt.yticks([])\n\nplt.subplot(1, 3, 2)\nplt.imshow(plt.imread(all_images[10]))\nplt.xticks([])\nplt.yticks([])\n\nplt.subplot(1, 3, 3)\nplt.imshow(plt.imread(all_images[20]))\nplt.xticks([])\nplt.yticks([])\n\n```\n\nOut[6]:\n\n```\n([], <a list of 0 Text yticklabel objects>)\n```\n\n![](cactus-identification-ensemble-transfer-learning_files/__results___5_1.png)In [7]:\n\n```py\ntrain_path = '../input/train/train/'\ntest_path = '../input/test/test/'\n\n```\n\nIn [8]:\n\n```py\n#let's get our image data and image labels toegether\n#read in all the images\nimages_id = train_df['id'].values\nX = [] #this list will contain all our images\nfor id_ in images_id:\n    img = cv2.imread(train_path + id_)\n    X.append(img)\n\n```\n\nIn [9]:\n\n```py\n#now let's get our labels\nlabel_list = [] #will contain all our labels\nfor img_id in images_id:\n    label_list.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])\n\n```\n\nIn [10]:\n\n```py\n#now we can convert our images list and the labels list into numpy array\nX = np.array(X)\ny = np.array(label_list)\n\n```\n\nIn [11]:\n\n```py\nprint(f\"THE SIZE OF OUR TRAINING DATA : {X.shape}\")\nprint(f\"THE SIZE OF OUR TRAINING LABELS : {y.shape}\")\n\n```\n\n```\nTHE SIZE OF OUR TRAINING DATA : (17500, 32, 32, 3)\nTHE SIZE OF OUR TRAINING LABELS : (17500,)\n\n```\n\nIn [12]:\n\n```py\n#let's do some preprocessing such as normalizing our data\nX = X.astype('float32') / 255\n\n```\n\nIn [13]:\n\n```py\n#loading in and preprocessing the test data\nX_test = []\ntest_images = []\nfor img_id in tqdm_notebook(os.listdir(test_path)):\n    X_test.append(cv2.imread(test_path + img_id))     \n    test_images.append(img_id)\nX_test = np.array(X_test)\nX_test = X_test.astype('float32') / 255\n\n```\n\n## BUILD CNN\n\nIn [14]:\n\n```py\n#import the required libraries\nimport keras\nfrom keras.layers import Conv2D\nfrom keras.layers import Dense\nfrom keras.layers import BatchNormalization\nfrom keras.layers import Flatten\nfrom keras.layers import Activation\nfrom keras.layers import Dropout\nfrom keras.layers import MaxPooling2D\nfrom keras.models import Sequential\nfrom keras import optimizers\nfrom keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\nfrom keras import backend as K\n\n```\n\n```\nUsing TensorFlow backend.\n\n```\n\nIn [15]:\n\n```py\nclass CNN:\n    def build(height, width, classes, channels):\n        model = Sequential()\n        inputShape = (height, width, channels)\n        chanDim = -1\n\n        if K.image_data_format() == 'channels_first':\n            inputShape = (channels, height, width)\n            chanDim = 1\n        model.add(Conv2D(32, (3,3), padding = 'same', input_shape = inputShape))\n        model.add(BatchNormalization(axis = chanDim))\n        model.add(Activation('relu'))\n        model.add(Conv2D(32, (3,3), padding = 'same'))\n        model.add(BatchNormalization(axis = chanDim))\n        model.add(Activation('relu'))\n        model.add(MaxPooling2D(2,2))\n        model.add(Dropout(0.25))\n\n        model.add(Conv2D(128, (3,3), padding = 'same', input_shape = inputShape))\n        model.add(BatchNormalization(axis = chanDim))\n        model.add(Activation('relu'))\n        model.add(Conv2D(128, (3,3), padding = 'same'))\n        model.add(BatchNormalization(axis = chanDim))\n        model.add(Activation('relu'))\n        model.add(MaxPooling2D(2,2))\n        model.add(Dropout(0.25))\n\n        model.add(Conv2D(256, (3,3), padding = 'same', input_shape = inputShape))\n        model.add(BatchNormalization(axis = chanDim))\n        model.add(Activation('relu'))\n        model.add(Conv2D(256, (3,3), padding = 'same'))\n        model.add(BatchNormalization(axis = chanDim))\n        model.add(Activation('relu'))\n        model.add(MaxPooling2D(2,2))\n        model.add(Dropout(0.25))\n\n        model.add(Flatten())\n\n        model.add(Dense(128, activation = 'relu'))\n        model.add(BatchNormalization(axis = chanDim))\n        model.add(Dropout(0.5))\n\n        model.add(Dense(32, activation = 'relu'))\n        model.add(BatchNormalization(axis = chanDim))\n        model.add(Dropout(0.5))\n\n        model.add(Dense(classes, activation = 'sigmoid'))\n\n        return model\n\n```\n\n## ENSEMBLE NEURAL NETWORK\n\nOutputIn [16]:\n\n```py\ninput_dim = X.shape[1:]\nactivation = 'relu'\nclasses = 1\nheight = 32\nwidth = 32\nchannels = 3\n\nhistory = dict() #dictionery to store the history of individual models for later visualization\nprediction_scores = dict() #dictionery to store the predicted scores of individual models on the test dataset\n\n#here we will be training the same model for a total of 10 times and will be considering the mean of the output values for predictions\nfor i in np.arange(0, 5):\n    optim = optimizers.Adam(lr = 0.001)\n    ensemble_model = CNN.build(height = height, width = width, classes = classes, channels = channels)\n    ensemble_model.compile(loss = 'binary_crossentropy', optimizer = optim, metrics = ['accuracy'])\n    print('TRAINING MODEL NO : {}'.format(i))\n    H = ensemble_model.fit(X, y,\n                           batch_size = 32,\n                           epochs = 200,\n                           verbose = 1)\n    history[i] = H\n\n    ensemble_model.save('MODEL_{}.model'.format(i))\n\n    predictions = ensemble_model.predict(X_test, verbose = 1, batch_size = 32)\n    prediction_scores[i] = predictions\n\n```\n\n```\nTRAINING MODEL NO : 0\nEpoch 1/200\n17500/17500 [==============================] - 12s 711us/step - loss: 0.2101 - acc: 0.9271\nEpoch 2/200\n17500/17500 [==============================] - 8s 461us/step - loss: 0.0847 - acc: 0.9743\nEpoch 3/200\n17500/17500 [==============================] - 8s 467us/step - loss: 0.0669 - acc: 0.9787\nEpoch 4/200\n17500/17500 [==============================] - 8s 460us/step - loss: 0.0560 - acc: 0.9831\nEpoch 5/200\n17500/17500 [==============================] - 8s 463us/step - loss: 0.0398 - acc: 0.9883\nEpoch 6/200\n17500/17500 [==============================] - 8s 461us/step - loss: 0.0435 - acc: 0.9875\nEpoch 7/200\n17500/17500 [==============================] - 8s 460us/step - loss: 0.0310 - acc: 0.9905\nEpoch 8/200\n17500/17500 [==============================] - 8s 461us/step - loss: 0.0294 - acc: 0.9914\nEpoch 9/200\n17500/17500 [==============================] - 8s 457us/step - loss: 0.0223 - acc: 0.9937\nEpoch 10/200\n17500/17500 [==============================] - 8s 458us/step - loss: 0.0260 - acc: 0.9921\nEpoch 11/200\n17500/17500 [==============================] - 8s 460us/step - loss: 0.0248 - acc: 0.9931\nEpoch 12/200\n17500/17500 [==============================] - 8s 460us/step - loss: 0.0168 - acc: 0.9946\nEpoch 13/200\n17500/17500 [==============================] - 8s 460us/step - loss: 0.0230 - acc: 0.9933\nEpoch 14/200\n17500/17500 [==============================] - 8s 458us/step - loss: 0.0149 - acc: 0.9955\nEpoch 15/200\n17500/17500 [==============================] - 8s 458us/step - loss: 0.0171 - acc: 0.9949\nEpoch 16/200\n17500/17500 [==============================] - 8s 459us/step - loss: 0.0141 - acc: 0.9964\nEpoch 17/200\n17500/17500 [==============================] - 8s 458us/step - loss: 0.0107 - acc: 0.9969\nEpoch 18/200\n17500/17500 [==============================] - 8s 460us/step - loss: 0.0118 - acc: 0.9966\nEpoch 19/200\n17500/17500 [==============================] - 8s 459us/step - loss: 0.0116 - acc: 0.9966\nEpoch 20/200\n17500/17500 [==============================] - 8s 458us/step - loss: 0.0113 - acc: 0.9966\nEpoch 21/200\n17500/17500 [==============================] - 8s 458us/step - loss: 0.0081 - acc: 0.9980\nEpoch 22/200\n17500/17500 [==============================] - 8s 460us/step - loss: 0.0102 - acc: 0.9969\nEpoch 23/200\n17500/17500 [==============================] - 8s 459us/step - loss: 0.0080 - acc: 0.9978\nEpoch 24/200\n17500/17500 [==============================] - 8s 460us/step - loss: 0.0103 - acc: 0.9973\nEpoch 25/200\n17500/17500 [==============================] - 8s 460us/step - loss: 0.0071 - acc: 0.9981\nEpoch 26/200\n17500/17500 [==============================] - 9s 504us/step - loss: 0.0060 - acc: 0.9982\nEpoch 27/200\n17500/17500 [==============================] - 8s 458us/step - loss: 0.0087 - acc: 0.9975\nEpoch 28/200\n17500/17500 [==============================] - 8s 457us/step - loss: 0.0079 - acc: 0.9974\nEpoch 29/200\n17500/17500 [==============================] - 8s 461us/step - loss: 0.0036 - acc: 0.9987\nEpoch 30/200\n17500/17500 [==============================] - 8s 458us/step - loss: 0.0045 - acc: 0.9986\nEpoch 31/200\n17500/17500 [==============================] - 8s 457us/step - loss: 0.0095 - acc: 0.9975\nEpoch 32/200\n17500/17500 [==============================] - 9s 494us/step - loss: 0.0064 - acc: 0.9976\nEpoch 33/200\n17500/17500 [==============================] - 9s 514us/step - loss: 0.0058 - acc: 0.9982\nEpoch 34/200\n17500/17500 [==============================] - 8s 460us/step - loss: 0.0061 - acc: 0.9984\nEpoch 35/200\n17500/17500 [==============================] - 8s 459us/step - loss: 0.0015 - acc: 0.9997\nEpoch 36/200\n17500/17500 [==============================] - 8s 459us/step - loss: 0.0065 - acc: 0.9981\nEpoch 37/200\n17500/17500 [==============================] - 8s 461us/step - loss: 0.0058 - acc: 0.9982\nEpoch 38/200\n17500/17500 [==============================] - 8s 459us/step - loss: 0.0035 - acc: 0.9989\nEpoch 39/200\n17500/17500 [==============================] - 8s 460us/step - loss: 0.0030 - acc: 0.9991\nEpoch 40/200\n17500/17500 [==============================] - 8s 459us/step - loss: 0.0049 - acc: 0.9985\nEpoch 41/200\n17500/17500 [==============================] - 8s 465us/step - loss: 0.0044 - acc: 0.9987\nEpoch 42/200\n17500/17500 [==============================] - 8s 459us/step - loss: 0.0032 - acc: 0.9990\nEpoch 43/200\n17500/17500 [==============================] - 8s 458us/step - loss: 0.0086 - acc: 0.9974\nEpoch 44/200\n17500/17500 [==============================] - 8s 460us/step - loss: 0.0027 - acc: 0.9991\nEpoch 45/200\n17500/17500 [==============================] - 8s 461us/step - loss: 0.0020 - acc: 0.9997\nEpoch 46/200\n 3488/17500 [====>.........................] - ETA: 6s - loss: 2.9141e-04 - acc: 1.0000\n```\n\n## VGG16\n\nIn [17]:\n\n```py\nfrom keras.applications.vgg16 import VGG16\n\n```\n\nIn [18]:\n\n```py\nvgg16 = VGG16(weights = 'imagenet', input_shape = (32, 32, 3), include_top = False)\nvgg16.summary()\n\n```\n\n```\nDownloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n58892288/58889256 [==============================] - 1s 0us/step\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_1 (InputLayer)         (None, 32, 32, 3)         0         \n_________________________________________________________________\nblock1_conv1 (Conv2D)        (None, 32, 32, 64)        1792      \n_________________________________________________________________\nblock1_conv2 (Conv2D)        (None, 32, 32, 64)        36928     \n_________________________________________________________________\nblock1_pool (MaxPooling2D)   (None, 16, 16, 64)        0         \n_________________________________________________________________\nblock2_conv1 (Conv2D)        (None, 16, 16, 128)       73856     \n_________________________________________________________________\nblock2_conv2 (Conv2D)        (None, 16, 16, 128)       147584    \n_________________________________________________________________\nblock2_pool (MaxPooling2D)   (None, 8, 8, 128)         0         \n_________________________________________________________________\nblock3_conv1 (Conv2D)        (None, 8, 8, 256)         295168    \n_________________________________________________________________\nblock3_conv2 (Conv2D)        (None, 8, 8, 256)         590080    \n_________________________________________________________________\nblock3_conv3 (Conv2D)        (None, 8, 8, 256)         590080    \n_________________________________________________________________\nblock3_pool (MaxPooling2D)   (None, 4, 4, 256)         0         \n_________________________________________________________________\nblock4_conv1 (Conv2D)        (None, 4, 4, 512)         1180160   \n_________________________________________________________________\nblock4_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n_________________________________________________________________\nblock4_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n_________________________________________________________________\nblock4_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n_________________________________________________________________\nblock5_conv1 (Conv2D)        (None, 2, 2, 512)         2359808   \n_________________________________________________________________\nblock5_conv2 (Conv2D)        (None, 2, 2, 512)         2359808   \n_________________________________________________________________\nblock5_conv3 (Conv2D)        (None, 2, 2, 512)         2359808   \n_________________________________________________________________\nblock5_pool (MaxPooling2D)   (None, 1, 1, 512)         0         \n=================================================================\nTotal params: 14,714,688\nTrainable params: 14,714,688\nNon-trainable params: 0\n_________________________________________________________________\n\n```\n\nIn [19]:\n\n```py\nfor layer in vgg16.layers:\n    layer.trainable = False\n\n```\n\nIn [20]:\n\n```py\nvgg_model = Sequential()\nvgg_model.add(vgg16)\nvgg_model.add(Flatten())\nvgg_model.add(Dense(256, activation = 'relu'))\nvgg_model.add(BatchNormalization())\nvgg_model.add(Dropout(0.5))\nvgg_model.add(Dense(128, activation = 'relu'))\nvgg_model.add(BatchNormalization())\nvgg_model.add(Dropout(0.5))\nvgg_model.add(Dense(1, activation = 'sigmoid'))\n\nvgg_model.summary()\n\n```\n\n```\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nvgg16 (Model)                (None, 1, 1, 512)         14714688  \n_________________________________________________________________\nflatten_6 (Flatten)          (None, 512)               0         \n_________________________________________________________________\ndense_16 (Dense)             (None, 256)               131328    \n_________________________________________________________________\nbatch_normalization_41 (Batc (None, 256)               1024      \n_________________________________________________________________\ndropout_26 (Dropout)         (None, 256)               0         \n_________________________________________________________________\ndense_17 (Dense)             (None, 128)               32896     \n_________________________________________________________________\nbatch_normalization_42 (Batc (None, 128)               512       \n_________________________________________________________________\ndropout_27 (Dropout)         (None, 128)               0         \n_________________________________________________________________\ndense_18 (Dense)             (None, 1)                 129       \n=================================================================\nTotal params: 14,880,577\nTrainable params: 165,121\nNon-trainable params: 14,715,456\n_________________________________________________________________\n\n```\n\nIn [21]:\n\n```py\n#compile the model\nvgg_model.compile(loss = 'binary_crossentropy', optimizer = optim, metrics = ['accuracy'])\n\n```\n\nIn [22]:\n\n```py\n#fit the model on our data\nvgg_history = vgg_model.fit(X, y,\n                            batch_size = 64,\n                            epochs = 500,\n                            verbose = 1) \n\n```\n\n```\nEpoch 1/500\n17500/17500 [==============================] - 6s 320us/step - loss: 0.1719 - acc: 0.9358\nEpoch 2/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.1074 - acc: 0.9611\nEpoch 3/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0990 - acc: 0.9636\nEpoch 4/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0892 - acc: 0.9658\nEpoch 5/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0869 - acc: 0.9683\nEpoch 6/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0828 - acc: 0.9701\nEpoch 7/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0766 - acc: 0.9723\nEpoch 8/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0802 - acc: 0.9686\nEpoch 9/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0755 - acc: 0.9718\nEpoch 10/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0752 - acc: 0.9714\nEpoch 11/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0719 - acc: 0.9731\nEpoch 12/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0718 - acc: 0.9762\nEpoch 13/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0702 - acc: 0.9744\nEpoch 14/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0694 - acc: 0.9745\nEpoch 15/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0687 - acc: 0.9751\nEpoch 16/500\n17500/17500 [==============================] - 3s 200us/step - loss: 0.0699 - acc: 0.9752\nEpoch 17/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0667 - acc: 0.9755\nEpoch 18/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0639 - acc: 0.9765\nEpoch 19/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0636 - acc: 0.9763\nEpoch 20/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0661 - acc: 0.9759\nEpoch 21/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0607 - acc: 0.9777\nEpoch 22/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0627 - acc: 0.9763\nEpoch 23/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0609 - acc: 0.9781\nEpoch 24/500\n17500/17500 [==============================] - 4s 205us/step - loss: 0.0618 - acc: 0.9776\nEpoch 25/500\n17500/17500 [==============================] - 4s 229us/step - loss: 0.0636 - acc: 0.9764\nEpoch 26/500\n17500/17500 [==============================] - 4s 229us/step - loss: 0.0623 - acc: 0.9777\nEpoch 27/500\n17500/17500 [==============================] - 4s 215us/step - loss: 0.0576 - acc: 0.9797\nEpoch 28/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0604 - acc: 0.9779\nEpoch 29/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0590 - acc: 0.9782\nEpoch 30/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0600 - acc: 0.9779\nEpoch 31/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0557 - acc: 0.9782\nEpoch 32/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0586 - acc: 0.9778\nEpoch 33/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0575 - acc: 0.9791\nEpoch 34/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0586 - acc: 0.9777\nEpoch 35/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0609 - acc: 0.9779\nEpoch 36/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0544 - acc: 0.9805\nEpoch 37/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0550 - acc: 0.9800\nEpoch 38/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0560 - acc: 0.9797\nEpoch 39/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0554 - acc: 0.9796\nEpoch 40/500\n17500/17500 [==============================] - 4s 204us/step - loss: 0.0581 - acc: 0.9791\nEpoch 41/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0549 - acc: 0.9795\nEpoch 42/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0544 - acc: 0.9799\nEpoch 43/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0544 - acc: 0.9801\nEpoch 44/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0534 - acc: 0.9803\nEpoch 45/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0526 - acc: 0.9812\nEpoch 46/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0547 - acc: 0.9801\nEpoch 47/500\n17500/17500 [==============================] - 4s 204us/step - loss: 0.0536 - acc: 0.9800\nEpoch 48/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0492 - acc: 0.9817\nEpoch 49/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0553 - acc: 0.9802\nEpoch 50/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0502 - acc: 0.9820\nEpoch 51/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0505 - acc: 0.9823\nEpoch 52/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0534 - acc: 0.9801\nEpoch 53/500\n17500/17500 [==============================] - 4s 204us/step - loss: 0.0505 - acc: 0.9809\nEpoch 54/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0460 - acc: 0.9831\nEpoch 55/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0492 - acc: 0.9819\nEpoch 56/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0481 - acc: 0.9826\nEpoch 57/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0515 - acc: 0.9814\nEpoch 58/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0495 - acc: 0.9816\nEpoch 59/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0476 - acc: 0.9837\nEpoch 60/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0483 - acc: 0.9821\nEpoch 61/500\n17500/17500 [==============================] - 4s 216us/step - loss: 0.0456 - acc: 0.9830\nEpoch 62/500\n17500/17500 [==============================] - 4s 227us/step - loss: 0.0472 - acc: 0.9829\nEpoch 63/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0462 - acc: 0.9833\nEpoch 64/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0496 - acc: 0.9828\nEpoch 65/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0482 - acc: 0.9826\nEpoch 66/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0477 - acc: 0.9828\nEpoch 67/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0465 - acc: 0.9825\nEpoch 68/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0481 - acc: 0.9827\nEpoch 69/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0470 - acc: 0.9830\nEpoch 70/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0469 - acc: 0.9833\nEpoch 71/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0470 - acc: 0.9819\nEpoch 72/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0471 - acc: 0.9829\nEpoch 73/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0466 - acc: 0.9835\nEpoch 74/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0461 - acc: 0.9838\nEpoch 75/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0439 - acc: 0.9841\nEpoch 76/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0462 - acc: 0.9830\nEpoch 77/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0465 - acc: 0.9831\nEpoch 78/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0452 - acc: 0.9832\nEpoch 79/500\n17500/17500 [==============================] - 4s 205us/step - loss: 0.0452 - acc: 0.9829\nEpoch 80/500\n17500/17500 [==============================] - 4s 204us/step - loss: 0.0444 - acc: 0.9835\nEpoch 81/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0463 - acc: 0.9823\nEpoch 82/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0461 - acc: 0.9829\nEpoch 83/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0436 - acc: 0.9845\nEpoch 84/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0468 - acc: 0.9837\nEpoch 85/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0405 - acc: 0.9850\nEpoch 86/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0416 - acc: 0.9845\nEpoch 87/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0419 - acc: 0.9842\nEpoch 88/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0439 - acc: 0.9841\nEpoch 89/500\n17500/17500 [==============================] - 4s 200us/step - loss: 0.0393 - acc: 0.9858\nEpoch 90/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0440 - acc: 0.9853\nEpoch 91/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0428 - acc: 0.9847\nEpoch 92/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0451 - acc: 0.9838\nEpoch 93/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0410 - acc: 0.9849\nEpoch 94/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0419 - acc: 0.9846\nEpoch 95/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0427 - acc: 0.9847\nEpoch 96/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0394 - acc: 0.9855\nEpoch 97/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0419 - acc: 0.9844\nEpoch 98/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0412 - acc: 0.9850\nEpoch 99/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0390 - acc: 0.9859\nEpoch 100/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0442 - acc: 0.9843\nEpoch 101/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0414 - acc: 0.9853\nEpoch 102/500\n17500/17500 [==============================] - 4s 204us/step - loss: 0.0414 - acc: 0.9844\nEpoch 103/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0390 - acc: 0.9864\nEpoch 104/500\n17500/17500 [==============================] - 4s 204us/step - loss: 0.0426 - acc: 0.9846\nEpoch 105/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0394 - acc: 0.9859\nEpoch 106/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0399 - acc: 0.9861\nEpoch 107/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0398 - acc: 0.9857\nEpoch 108/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0426 - acc: 0.9851\nEpoch 109/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0407 - acc: 0.9852\nEpoch 110/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0421 - acc: 0.9847\nEpoch 111/500\n17500/17500 [==============================] - 4s 204us/step - loss: 0.0386 - acc: 0.9860\nEpoch 112/500\n17500/17500 [==============================] - 4s 227us/step - loss: 0.0422 - acc: 0.9847\nEpoch 113/500\n17500/17500 [==============================] - 4s 230us/step - loss: 0.0382 - acc: 0.9863\nEpoch 114/500\n17500/17500 [==============================] - 4s 219us/step - loss: 0.0386 - acc: 0.9865\nEpoch 115/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0391 - acc: 0.9857\nEpoch 116/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0406 - acc: 0.9843\nEpoch 117/500\n17500/17500 [==============================] - 4s 200us/step - loss: 0.0376 - acc: 0.9864\nEpoch 118/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0397 - acc: 0.9853\nEpoch 119/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0394 - acc: 0.9861\nEpoch 120/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0365 - acc: 0.9866\nEpoch 121/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0391 - acc: 0.9855\nEpoch 122/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0383 - acc: 0.9861\nEpoch 123/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0360 - acc: 0.9874\nEpoch 124/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0357 - acc: 0.9869\nEpoch 125/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0376 - acc: 0.9862\nEpoch 126/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0389 - acc: 0.9855\nEpoch 127/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0405 - acc: 0.9852\nEpoch 128/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0370 - acc: 0.9861\nEpoch 129/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0378 - acc: 0.9867\nEpoch 130/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0367 - acc: 0.9861\nEpoch 131/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0375 - acc: 0.9861\nEpoch 132/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0376 - acc: 0.9860\nEpoch 133/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0380 - acc: 0.9867\nEpoch 134/500\n17500/17500 [==============================] - 3s 200us/step - loss: 0.0394 - acc: 0.9866\nEpoch 135/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0371 - acc: 0.9875\nEpoch 136/500\n17500/17500 [==============================] - 4s 204us/step - loss: 0.0399 - acc: 0.9859\nEpoch 137/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0377 - acc: 0.9874\nEpoch 138/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0368 - acc: 0.9874\nEpoch 139/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0400 - acc: 0.9851\nEpoch 140/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0395 - acc: 0.9854\nEpoch 141/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0366 - acc: 0.9869\nEpoch 142/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0375 - acc: 0.9871\nEpoch 143/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0354 - acc: 0.9881\nEpoch 144/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0367 - acc: 0.9861\nEpoch 145/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0380 - acc: 0.9864\nEpoch 146/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0352 - acc: 0.9876\nEpoch 147/500\n17500/17500 [==============================] - 4s 221us/step - loss: 0.0343 - acc: 0.9874\nEpoch 148/500\n17500/17500 [==============================] - 4s 219us/step - loss: 0.0351 - acc: 0.9873\nEpoch 149/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0364 - acc: 0.9863\nEpoch 150/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0376 - acc: 0.9870\nEpoch 151/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0385 - acc: 0.9869\nEpoch 152/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0342 - acc: 0.9883\nEpoch 153/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0355 - acc: 0.9867\nEpoch 154/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0338 - acc: 0.9874\nEpoch 155/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0360 - acc: 0.9868\nEpoch 156/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0329 - acc: 0.9874\nEpoch 157/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0383 - acc: 0.9856\nEpoch 158/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0362 - acc: 0.9872\nEpoch 159/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0361 - acc: 0.9862\nEpoch 160/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0361 - acc: 0.9866\nEpoch 161/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0342 - acc: 0.9875\nEpoch 162/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0348 - acc: 0.9873\nEpoch 163/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0350 - acc: 0.9871\nEpoch 164/500\n17500/17500 [==============================] - 4s 206us/step - loss: 0.0364 - acc: 0.9859\nEpoch 165/500\n17500/17500 [==============================] - 4s 205us/step - loss: 0.0317 - acc: 0.9881\nEpoch 166/500\n17500/17500 [==============================] - 3s 200us/step - loss: 0.0355 - acc: 0.9872\nEpoch 167/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0406 - acc: 0.9864\nEpoch 168/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0364 - acc: 0.9866\nEpoch 169/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0352 - acc: 0.9879\nEpoch 170/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0366 - acc: 0.9869\nEpoch 171/500\n17500/17500 [==============================] - 4s 203us/step - loss: 0.0362 - acc: 0.9872\nEpoch 172/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0359 - acc: 0.9866\nEpoch 173/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0345 - acc: 0.9869\nEpoch 174/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0364 - acc: 0.9877\nEpoch 175/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0339 - acc: 0.9875\nEpoch 176/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0356 - acc: 0.9871\nEpoch 177/500\n17500/17500 [==============================] - 4s 201us/step - loss: 0.0323 - acc: 0.9886\nEpoch 178/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0336 - acc: 0.9882\nEpoch 179/500\n17500/17500 [==============================] - 4s 202us/step - loss: 0.0336 - acc: 0.9886\nEpoch 180/500\n 4928/17500 [=======>......................] - ETA: 2s - loss: 0.0328 - acc: 0.9874\n```\n\nIn [23]:\n\n```py\n#making predictions on test dat\npredictions_vgg = vgg_model.predict(X_test)\n\n```\n\nIn [24]:\n\n```py\npredictions_vgg.shape\n\n```\n\nOut[24]:\n\n```\n(4000, 1)\n```\n\n## RESNET50\n\nIn [25]:\n\n```py\nfrom keras.applications.resnet50 import ResNet50\n\n```\n\nOutputIn [26]:\n\n```py\nresnet = ResNet50(weights = 'imagenet', input_shape = (32, 32, 3), include_top = False)\nresnet.summary()\n\n```\n\n```\n/opt/conda/lib/python3.6/site-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n\n```\n\n```\nDownloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n94658560/94653016 [==============================] - 1s 0us/step\n__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\ninput_2 (InputLayer)            (None, 32, 32, 3)    0                                            \n__________________________________________________________________________________________________\nconv1_pad (ZeroPadding2D)       (None, 38, 38, 3)    0           input_2[0][0]                    \n__________________________________________________________________________________________________\nconv1 (Conv2D)                  (None, 16, 16, 64)   9472        conv1_pad[0][0]                  \n__________________________________________________________________________________________________\nbn_conv1 (BatchNormalization)   (None, 16, 16, 64)   256         conv1[0][0]                      \n__________________________________________________________________________________________________\nactivation_31 (Activation)      (None, 16, 16, 64)   0           bn_conv1[0][0]                   \n__________________________________________________________________________________________________\npool1_pad (ZeroPadding2D)       (None, 18, 18, 64)   0           activation_31[0][0]              \n__________________________________________________________________________________________________\nmax_pooling2d_16 (MaxPooling2D) (None, 8, 8, 64)     0           pool1_pad[0][0]                  \n__________________________________________________________________________________________________\nres2a_branch2a (Conv2D)         (None, 8, 8, 64)     4160        max_pooling2d_16[0][0]           \n__________________________________________________________________________________________________\nbn2a_branch2a (BatchNormalizati (None, 8, 8, 64)     256         res2a_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_32 (Activation)      (None, 8, 8, 64)     0           bn2a_branch2a[0][0]              \n__________________________________________________________________________________________________\nres2a_branch2b (Conv2D)         (None, 8, 8, 64)     36928       activation_32[0][0]              \n__________________________________________________________________________________________________\nbn2a_branch2b (BatchNormalizati (None, 8, 8, 64)     256         res2a_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_33 (Activation)      (None, 8, 8, 64)     0           bn2a_branch2b[0][0]              \n__________________________________________________________________________________________________\nres2a_branch2c (Conv2D)         (None, 8, 8, 256)    16640       activation_33[0][0]              \n__________________________________________________________________________________________________\nres2a_branch1 (Conv2D)          (None, 8, 8, 256)    16640       max_pooling2d_16[0][0]           \n__________________________________________________________________________________________________\nbn2a_branch2c (BatchNormalizati (None, 8, 8, 256)    1024        res2a_branch2c[0][0]             \n__________________________________________________________________________________________________\nbn2a_branch1 (BatchNormalizatio (None, 8, 8, 256)    1024        res2a_branch1[0][0]              \n__________________________________________________________________________________________________\nadd_1 (Add)                     (None, 8, 8, 256)    0           bn2a_branch2c[0][0]              \n                                                                 bn2a_branch1[0][0]               \n__________________________________________________________________________________________________\nactivation_34 (Activation)      (None, 8, 8, 256)    0           add_1[0][0]                      \n__________________________________________________________________________________________________\nres2b_branch2a (Conv2D)         (None, 8, 8, 64)     16448       activation_34[0][0]              \n__________________________________________________________________________________________________\nbn2b_branch2a (BatchNormalizati (None, 8, 8, 64)     256         res2b_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_35 (Activation)      (None, 8, 8, 64)     0           bn2b_branch2a[0][0]              \n__________________________________________________________________________________________________\nres2b_branch2b (Conv2D)         (None, 8, 8, 64)     36928       activation_35[0][0]              \n__________________________________________________________________________________________________\nbn2b_branch2b (BatchNormalizati (None, 8, 8, 64)     256         res2b_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_36 (Activation)      (None, 8, 8, 64)     0           bn2b_branch2b[0][0]              \n__________________________________________________________________________________________________\nres2b_branch2c (Conv2D)         (None, 8, 8, 256)    16640       activation_36[0][0]              \n__________________________________________________________________________________________________\nbn2b_branch2c (BatchNormalizati (None, 8, 8, 256)    1024        res2b_branch2c[0][0]             \n__________________________________________________________________________________________________\nadd_2 (Add)                     (None, 8, 8, 256)    0           bn2b_branch2c[0][0]              \n                                                                 activation_34[0][0]              \n__________________________________________________________________________________________________\nactivation_37 (Activation)      (None, 8, 8, 256)    0           add_2[0][0]                      \n__________________________________________________________________________________________________\nres2c_branch2a (Conv2D)         (None, 8, 8, 64)     16448       activation_37[0][0]              \n__________________________________________________________________________________________________\nbn2c_branch2a (BatchNormalizati (None, 8, 8, 64)     256         res2c_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_38 (Activation)      (None, 8, 8, 64)     0           bn2c_branch2a[0][0]              \n__________________________________________________________________________________________________\nres2c_branch2b (Conv2D)         (None, 8, 8, 64)     36928       activation_38[0][0]              \n__________________________________________________________________________________________________\nbn2c_branch2b (BatchNormalizati (None, 8, 8, 64)     256         res2c_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_39 (Activation)      (None, 8, 8, 64)     0           bn2c_branch2b[0][0]              \n__________________________________________________________________________________________________\nres2c_branch2c (Conv2D)         (None, 8, 8, 256)    16640       activation_39[0][0]              \n__________________________________________________________________________________________________\nbn2c_branch2c (BatchNormalizati (None, 8, 8, 256)    1024        res2c_branch2c[0][0]             \n__________________________________________________________________________________________________\nadd_3 (Add)                     (None, 8, 8, 256)    0           bn2c_branch2c[0][0]              \n                                                                 activation_37[0][0]              \n__________________________________________________________________________________________________\nactivation_40 (Activation)      (None, 8, 8, 256)    0           add_3[0][0]                      \n__________________________________________________________________________________________________\nres3a_branch2a (Conv2D)         (None, 4, 4, 128)    32896       activation_40[0][0]              \n__________________________________________________________________________________________________\nbn3a_branch2a (BatchNormalizati (None, 4, 4, 128)    512         res3a_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_41 (Activation)      (None, 4, 4, 128)    0           bn3a_branch2a[0][0]              \n__________________________________________________________________________________________________\nres3a_branch2b (Conv2D)         (None, 4, 4, 128)    147584      activation_41[0][0]              \n__________________________________________________________________________________________________\nbn3a_branch2b (BatchNormalizati (None, 4, 4, 128)    512         res3a_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_42 (Activation)      (None, 4, 4, 128)    0           bn3a_branch2b[0][0]              \n__________________________________________________________________________________________________\nres3a_branch2c (Conv2D)         (None, 4, 4, 512)    66048       activation_42[0][0]              \n__________________________________________________________________________________________________\nres3a_branch1 (Conv2D)          (None, 4, 4, 512)    131584      activation_40[0][0]              \n__________________________________________________________________________________________________\nbn3a_branch2c (BatchNormalizati (None, 4, 4, 512)    2048        res3a_branch2c[0][0]             \n__________________________________________________________________________________________________\nbn3a_branch1 (BatchNormalizatio (None, 4, 4, 512)    2048        res3a_branch1[0][0]              \n__________________________________________________________________________________________________\nadd_4 (Add)                     (None, 4, 4, 512)    0           bn3a_branch2c[0][0]              \n                                                                 bn3a_branch1[0][0]               \n__________________________________________________________________________________________________\nactivation_43 (Activation)      (None, 4, 4, 512)    0           add_4[0][0]                      \n__________________________________________________________________________________________________\nres3b_branch2a (Conv2D)         (None, 4, 4, 128)    65664       activation_43[0][0]              \n__________________________________________________________________________________________________\nbn3b_branch2a (BatchNormalizati (None, 4, 4, 128)    512         res3b_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_44 (Activation)      (None, 4, 4, 128)    0           bn3b_branch2a[0][0]              \n__________________________________________________________________________________________________\nres3b_branch2b (Conv2D)         (None, 4, 4, 128)    147584      activation_44[0][0]              \n__________________________________________________________________________________________________\nbn3b_branch2b (BatchNormalizati (None, 4, 4, 128)    512         res3b_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_45 (Activation)      (None, 4, 4, 128)    0           bn3b_branch2b[0][0]              \n__________________________________________________________________________________________________\nres3b_branch2c (Conv2D)         (None, 4, 4, 512)    66048       activation_45[0][0]              \n__________________________________________________________________________________________________\nbn3b_branch2c (BatchNormalizati (None, 4, 4, 512)    2048        res3b_branch2c[0][0]             \n__________________________________________________________________________________________________\nadd_5 (Add)                     (None, 4, 4, 512)    0           bn3b_branch2c[0][0]              \n                                                                 activation_43[0][0]              \n__________________________________________________________________________________________________\nactivation_46 (Activation)      (None, 4, 4, 512)    0           add_5[0][0]                      \n__________________________________________________________________________________________________\nres3c_branch2a (Conv2D)         (None, 4, 4, 128)    65664       activation_46[0][0]              \n__________________________________________________________________________________________________\nbn3c_branch2a (BatchNormalizati (None, 4, 4, 128)    512         res3c_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_47 (Activation)      (None, 4, 4, 128)    0           bn3c_branch2a[0][0]              \n__________________________________________________________________________________________________\nres3c_branch2b (Conv2D)         (None, 4, 4, 128)    147584      activation_47[0][0]              \n__________________________________________________________________________________________________\nbn3c_branch2b (BatchNormalizati (None, 4, 4, 128)    512         res3c_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_48 (Activation)      (None, 4, 4, 128)    0           bn3c_branch2b[0][0]              \n__________________________________________________________________________________________________\nres3c_branch2c (Conv2D)         (None, 4, 4, 512)    66048       activation_48[0][0]              \n__________________________________________________________________________________________________\nbn3c_branch2c (BatchNormalizati (None, 4, 4, 512)    2048        res3c_branch2c[0][0]             \n__________________________________________________________________________________________________\nadd_6 (Add)                     (None, 4, 4, 512)    0           bn3c_branch2c[0][0]              \n                                                                 activation_46[0][0]              \n__________________________________________________________________________________________________\nactivation_49 (Activation)      (None, 4, 4, 512)    0           add_6[0][0]                      \n__________________________________________________________________________________________________\nres3d_branch2a (Conv2D)         (None, 4, 4, 128)    65664       activation_49[0][0]              \n__________________________________________________________________________________________________\nbn3d_branch2a (BatchNormalizati (None, 4, 4, 128)    512         res3d_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_50 (Activation)      (None, 4, 4, 128)    0           bn3d_branch2a[0][0]              \n__________________________________________________________________________________________________\nres3d_branch2b (Conv2D)         (None, 4, 4, 128)    147584      activation_50[0][0]              \n__________________________________________________________________________________________________\nbn3d_branch2b (BatchNormalizati (None, 4, 4, 128)    512         res3d_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_51 (Activation)      (None, 4, 4, 128)    0           bn3d_branch2b[0][0]              \n__________________________________________________________________________________________________\nres3d_branch2c (Conv2D)         (None, 4, 4, 512)    66048       activation_51[0][0]              \n__________________________________________________________________________________________________\nbn3d_branch2c (BatchNormalizati (None, 4, 4, 512)    2048        res3d_branch2c[0][0]             \n__________________________________________________________________________________________________\nadd_7 (Add)                     (None, 4, 4, 512)    0           bn3d_branch2c[0][0]              \n                                                                 activation_49[0][0]              \n__________________________________________________________________________________________________\nactivation_52 (Activation)      (None, 4, 4, 512)    0           add_7[0][0]                      \n__________________________________________________________________________________________________\nres4a_branch2a (Conv2D)         (None, 2, 2, 256)    131328      activation_52[0][0]              \n__________________________________________________________________________________________________\nbn4a_branch2a (BatchNormalizati (None, 2, 2, 256)    1024        res4a_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_53 (Activation)      (None, 2, 2, 256)    0           bn4a_branch2a[0][0]              \n__________________________________________________________________________________________________\nres4a_branch2b (Conv2D)         (None, 2, 2, 256)    590080      activation_53[0][0]              \n__________________________________________________________________________________________________\nbn4a_branch2b (BatchNormalizati (None, 2, 2, 256)    1024        res4a_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_54 (Activation)      (None, 2, 2, 256)    0           bn4a_branch2b[0][0]              \n__________________________________________________________________________________________________\nres4a_branch2c (Conv2D)         (None, 2, 2, 1024)   263168      activation_54[0][0]              \n__________________________________________________________________________________________________\nres4a_branch1 (Conv2D)          (None, 2, 2, 1024)   525312      activation_52[0][0]              \n__________________________________________________________________________________________________\nbn4a_branch2c (BatchNormalizati (None, 2, 2, 1024)   4096        res4a_branch2c[0][0]             \n__________________________________________________________________________________________________\nbn4a_branch1 (BatchNormalizatio (None, 2, 2, 1024)   4096        res4a_branch1[0][0]              \n__________________________________________________________________________________________________\nadd_8 (Add)                     (None, 2, 2, 1024)   0           bn4a_branch2c[0][0]              \n                                                                 bn4a_branch1[0][0]               \n__________________________________________________________________________________________________\nactivation_55 (Activation)      (None, 2, 2, 1024)   0           add_8[0][0]                      \n__________________________________________________________________________________________________\nres4b_branch2a (Conv2D)         (None, 2, 2, 256)    262400      activation_55[0][0]              \n__________________________________________________________________________________________________\nbn4b_branch2a (BatchNormalizati (None, 2, 2, 256)    1024        res4b_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_56 (Activation)      (None, 2, 2, 256)    0           bn4b_branch2a[0][0]              \n__________________________________________________________________________________________________\nres4b_branch2b (Conv2D)         (None, 2, 2, 256)    590080      activation_56[0][0]              \n__________________________________________________________________________________________________\nbn4b_branch2b (BatchNormalizati (None, 2, 2, 256)    1024        res4b_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_57 (Activation)      (None, 2, 2, 256)    0           bn4b_branch2b[0][0]              \n__________________________________________________________________________________________________\nres4b_branch2c (Conv2D)         (None, 2, 2, 1024)   263168      activation_57[0][0]              \n__________________________________________________________________________________________________\nbn4b_branch2c (BatchNormalizati (None, 2, 2, 1024)   4096        res4b_branch2c[0][0]             \n__________________________________________________________________________________________________\nadd_9 (Add)                     (None, 2, 2, 1024)   0           bn4b_branch2c[0][0]              \n                                                                 activation_55[0][0]              \n__________________________________________________________________________________________________\nactivation_58 (Activation)      (None, 2, 2, 1024)   0           add_9[0][0]                      \n__________________________________________________________________________________________________\nres4c_branch2a (Conv2D)         (None, 2, 2, 256)    262400      activation_58[0][0]              \n__________________________________________________________________________________________________\nbn4c_branch2a (BatchNormalizati (None, 2, 2, 256)    1024        res4c_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_59 (Activation)      (None, 2, 2, 256)    0           bn4c_branch2a[0][0]              \n__________________________________________________________________________________________________\nres4c_branch2b (Conv2D)         (None, 2, 2, 256)    590080      activation_59[0][0]              \n__________________________________________________________________________________________________\nbn4c_branch2b (BatchNormalizati (None, 2, 2, 256)    1024        res4c_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_60 (Activation)      (None, 2, 2, 256)    0           bn4c_branch2b[0][0]              \n__________________________________________________________________________________________________\nres4c_branch2c (Conv2D)         (None, 2, 2, 1024)   263168      activation_60[0][0]              \n__________________________________________________________________________________________________\nbn4c_branch2c (BatchNormalizati (None, 2, 2, 1024)   4096        res4c_branch2c[0][0]             \n__________________________________________________________________________________________________\nadd_10 (Add)                    (None, 2, 2, 1024)   0           bn4c_branch2c[0][0]              \n                                                                 activation_58[0][0]              \n__________________________________________________________________________________________________\nactivation_61 (Activation)      (None, 2, 2, 1024)   0           add_10[0][0]                     \n__________________________________________________________________________________________________\nres4d_branch2a (Conv2D)         (None, 2, 2, 256)    262400      activation_61[0][0]              \n__________________________________________________________________________________________________\nbn4d_branch2a (BatchNormalizati (None, 2, 2, 256)    1024        res4d_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_62 (Activation)      (None, 2, 2, 256)    0           bn4d_branch2a[0][0]              \n__________________________________________________________________________________________________\nres4d_branch2b (Conv2D)         (None, 2, 2, 256)    590080      activation_62[0][0]              \n__________________________________________________________________________________________________\nbn4d_branch2b (BatchNormalizati (None, 2, 2, 256)    1024        res4d_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_63 (Activation)      (None, 2, 2, 256)    0           bn4d_branch2b[0][0]              \n__________________________________________________________________________________________________\nres4d_branch2c (Conv2D)         (None, 2, 2, 1024)   263168      activation_63[0][0]              \n__________________________________________________________________________________________________\nbn4d_branch2c (BatchNormalizati (None, 2, 2, 1024)   4096        res4d_branch2c[0][0]             \n__________________________________________________________________________________________________\nadd_11 (Add)                    (None, 2, 2, 1024)   0           bn4d_branch2c[0][0]              \n                                                                 activation_61[0][0]              \n__________________________________________________________________________________________________\nactivation_64 (Activation)      (None, 2, 2, 1024)   0           add_11[0][0]                     \n__________________________________________________________________________________________________\nres4e_branch2a (Conv2D)         (None, 2, 2, 256)    262400      activation_64[0][0]              \n__________________________________________________________________________________________________\nbn4e_branch2a (BatchNormalizati (None, 2, 2, 256)    1024        res4e_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_65 (Activation)      (None, 2, 2, 256)    0           bn4e_branch2a[0][0]              \n__________________________________________________________________________________________________\nres4e_branch2b (Conv2D)         (None, 2, 2, 256)    590080      activation_65[0][0]              \n__________________________________________________________________________________________________\nbn4e_branch2b (BatchNormalizati (None, 2, 2, 256)    1024        res4e_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_66 (Activation)      (None, 2, 2, 256)    0           bn4e_branch2b[0][0]              \n__________________________________________________________________________________________________\nres4e_branch2c (Conv2D)         (None, 2, 2, 1024)   263168      activation_66[0][0]              \n__________________________________________________________________________________________________\nbn4e_branch2c (BatchNormalizati (None, 2, 2, 1024)   4096        res4e_branch2c[0][0]             \n__________________________________________________________________________________________________\nadd_12 (Add)                    (None, 2, 2, 1024)   0           bn4e_branch2c[0][0]              \n                                                                 activation_64[0][0]              \n__________________________________________________________________________________________________\nactivation_67 (Activation)      (None, 2, 2, 1024)   0           add_12[0][0]                     \n__________________________________________________________________________________________________\nres4f_branch2a (Conv2D)         (None, 2, 2, 256)    262400      activation_67[0][0]              \n__________________________________________________________________________________________________\nbn4f_branch2a (BatchNormalizati (None, 2, 2, 256)    1024        res4f_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_68 (Activation)      (None, 2, 2, 256)    0           bn4f_branch2a[0][0]              \n__________________________________________________________________________________________________\nres4f_branch2b (Conv2D)         (None, 2, 2, 256)    590080      activation_68[0][0]              \n__________________________________________________________________________________________________\nbn4f_branch2b (BatchNormalizati (None, 2, 2, 256)    1024        res4f_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_69 (Activation)      (None, 2, 2, 256)    0           bn4f_branch2b[0][0]              \n__________________________________________________________________________________________________\nres4f_branch2c (Conv2D)         (None, 2, 2, 1024)   263168      activation_69[0][0]              \n__________________________________________________________________________________________________\nbn4f_branch2c (BatchNormalizati (None, 2, 2, 1024)   4096        res4f_branch2c[0][0]             \n__________________________________________________________________________________________________\nadd_13 (Add)                    (None, 2, 2, 1024)   0           bn4f_branch2c[0][0]              \n                                                                 activation_67[0][0]              \n__________________________________________________________________________________________________\nactivation_70 (Activation)      (None, 2, 2, 1024)   0           add_13[0][0]                     \n__________________________________________________________________________________________________\nres5a_branch2a (Conv2D)         (None, 1, 1, 512)    524800      activation_70[0][0]              \n__________________________________________________________________________________________________\nbn5a_branch2a (BatchNormalizati (None, 1, 1, 512)    2048        res5a_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_71 (Activation)      (None, 1, 1, 512)    0           bn5a_branch2a[0][0]              \n__________________________________________________________________________________________________\nres5a_branch2b (Conv2D)         (None, 1, 1, 512)    2359808     activation_71[0][0]              \n__________________________________________________________________________________________________\nbn5a_branch2b (BatchNormalizati (None, 1, 1, 512)    2048        res5a_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_72 (Activation)      (None, 1, 1, 512)    0           bn5a_branch2b[0][0]              \n__________________________________________________________________________________________________\nres5a_branch2c (Conv2D)         (None, 1, 1, 2048)   1050624     activation_72[0][0]              \n__________________________________________________________________________________________________\nres5a_branch1 (Conv2D)          (None, 1, 1, 2048)   2099200     activation_70[0][0]              \n__________________________________________________________________________________________________\nbn5a_branch2c (BatchNormalizati (None, 1, 1, 2048)   8192        res5a_branch2c[0][0]             \n__________________________________________________________________________________________________\nbn5a_branch1 (BatchNormalizatio (None, 1, 1, 2048)   8192        res5a_branch1[0][0]              \n__________________________________________________________________________________________________\nadd_14 (Add)                    (None, 1, 1, 2048)   0           bn5a_branch2c[0][0]              \n                                                                 bn5a_branch1[0][0]               \n__________________________________________________________________________________________________\nactivation_73 (Activation)      (None, 1, 1, 2048)   0           add_14[0][0]                     \n__________________________________________________________________________________________________\nres5b_branch2a (Conv2D)         (None, 1, 1, 512)    1049088     activation_73[0][0]              \n__________________________________________________________________________________________________\nbn5b_branch2a (BatchNormalizati (None, 1, 1, 512)    2048        res5b_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_74 (Activation)      (None, 1, 1, 512)    0           bn5b_branch2a[0][0]              \n__________________________________________________________________________________________________\nres5b_branch2b (Conv2D)         (None, 1, 1, 512)    2359808     activation_74[0][0]              \n__________________________________________________________________________________________________\nbn5b_branch2b (BatchNormalizati (None, 1, 1, 512)    2048        res5b_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_75 (Activation)      (None, 1, 1, 512)    0           bn5b_branch2b[0][0]              \n__________________________________________________________________________________________________\nres5b_branch2c (Conv2D)         (None, 1, 1, 2048)   1050624     activation_75[0][0]              \n__________________________________________________________________________________________________\nbn5b_branch2c (BatchNormalizati (None, 1, 1, 2048)   8192        res5b_branch2c[0][0]             \n__________________________________________________________________________________________________\nadd_15 (Add)                    (None, 1, 1, 2048)   0           bn5b_branch2c[0][0]              \n                                                                 activation_73[0][0]              \n__________________________________________________________________________________________________\nactivation_76 (Activation)      (None, 1, 1, 2048)   0           add_15[0][0]                     \n__________________________________________________________________________________________________\nres5c_branch2a (Conv2D)         (None, 1, 1, 512)    1049088     activation_76[0][0]              \n__________________________________________________________________________________________________\nbn5c_branch2a (BatchNormalizati (None, 1, 1, 512)    2048        res5c_branch2a[0][0]             \n__________________________________________________________________________________________________\nactivation_77 (Activation)      (None, 1, 1, 512)    0           bn5c_branch2a[0][0]              \n__________________________________________________________________________________________________\nres5c_branch2b (Conv2D)         (None, 1, 1, 512)    2359808     activation_77[0][0]              \n__________________________________________________________________________________________________\nbn5c_branch2b (BatchNormalizati (None, 1, 1, 512)    2048        res5c_branch2b[0][0]             \n__________________________________________________________________________________________________\nactivation_78 (Activation)      (None, 1, 1, 512)    0           bn5c_branch2b[0][0]              \n__________________________________________________________________________________________________\nres5c_branch2c (Conv2D)         (None, 1, 1, 2048)   1050624     activation_78[0][0]              \n__________________________________________________________________________________________________\nbn5c_branch2c (BatchNormalizati (None, 1, 1, 2048)   8192        res5c_branch2c[0][0]             \n__________________________________________________________________________________________________\nadd_16 (Add)                    (None, 1, 1, 2048)   0           bn5c_branch2c[0][0]              \n                                                                 activation_76[0][0]              \n__________________________________________________________________________________________________\nactivation_79 (Activation)      (None, 1, 1, 2048)   0           add_16[0][0]                     \n==================================================================================================\nTotal params: 23,587,712\nTrainable params: 23,534,592\nNon-trainable params: 53,120\n__________________________________________________________________________________________________\n\n```\n\nIn [27]:\n\n```py\nfor layer in resnet.layers:\n    layer.trainable = False\n\n```\n\nIn [28]:\n\n```py\nresnet_model = Sequential()\nresnet_model.add(resnet)\nresnet_model.add(Flatten())\nresnet_model.add(Dense(256, activation = 'relu'))\nresnet_model.add(BatchNormalization())\nresnet_model.add(Dropout(0.5))\nresnet_model.add(Dense(128, activation = 'relu'))\nresnet_model.add(BatchNormalization())\nresnet_model.add(Dropout(0.5))\nresnet_model.add(Dense(1, activation = 'sigmoid'))\n\nresnet_model.summary()\n\n```\n\n```\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nresnet50 (Model)             (None, 1, 1, 2048)        23587712  \n_________________________________________________________________\nflatten_7 (Flatten)          (None, 2048)              0         \n_________________________________________________________________\ndense_19 (Dense)             (None, 256)               524544    \n_________________________________________________________________\nbatch_normalization_43 (Batc (None, 256)               1024      \n_________________________________________________________________\ndropout_28 (Dropout)         (None, 256)               0         \n_________________________________________________________________\ndense_20 (Dense)             (None, 128)               32896     \n_________________________________________________________________\nbatch_normalization_44 (Batc (None, 128)               512       \n_________________________________________________________________\ndropout_29 (Dropout)         (None, 128)               0         \n_________________________________________________________________\ndense_21 (Dense)             (None, 1)                 129       \n=================================================================\nTotal params: 24,146,817\nTrainable params: 558,337\nNon-trainable params: 23,588,480\n_________________________________________________________________\n\n```\n\nIn [29]:\n\n```py\n#compile the model\nresnet_model.compile(loss = 'binary_crossentropy', optimizer = optim, metrics = ['accuracy'])\n\n```\n\nOutputIn [30]:\n\n```py\n#fit the model on our data\nresnet_history = resnet_model.fit(X, y,\n                                  batch_size = 64, \n                                  epochs = 500,\n                                  verbose = 1) \n\n```\n\n```\nEpoch 1/500\n17500/17500 [==============================] - 10s 574us/step - loss: 0.1821 - acc: 0.9312\nEpoch 2/500\n17500/17500 [==============================] - 5s 298us/step - loss: 0.1203 - acc: 0.9552\nEpoch 3/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0996 - acc: 0.9625\nEpoch 4/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0987 - acc: 0.9617\nEpoch 5/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0914 - acc: 0.9627\nEpoch 6/500\n17500/17500 [==============================] - 5s 299us/step - loss: 0.0881 - acc: 0.9666\nEpoch 7/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0886 - acc: 0.9674\nEpoch 8/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0824 - acc: 0.9683\nEpoch 9/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0769 - acc: 0.9713\nEpoch 10/500\n17500/17500 [==============================] - 5s 293us/step - loss: 0.0718 - acc: 0.9721\nEpoch 11/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0688 - acc: 0.9737\nEpoch 12/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0660 - acc: 0.9747\nEpoch 13/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0658 - acc: 0.9750\nEpoch 14/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0670 - acc: 0.9743\nEpoch 15/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0645 - acc: 0.9766\nEpoch 16/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0617 - acc: 0.9771\nEpoch 17/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0586 - acc: 0.9785\nEpoch 18/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0560 - acc: 0.9789\nEpoch 19/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0584 - acc: 0.9782\nEpoch 20/500\n17500/17500 [==============================] - 5s 293us/step - loss: 0.0611 - acc: 0.9762\nEpoch 21/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0542 - acc: 0.9798\nEpoch 22/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0596 - acc: 0.9769\nEpoch 23/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0545 - acc: 0.9807\nEpoch 24/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0498 - acc: 0.9818\nEpoch 25/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0532 - acc: 0.9797\nEpoch 26/500\n17500/17500 [==============================] - 5s 301us/step - loss: 0.0511 - acc: 0.9816\nEpoch 27/500\n17500/17500 [==============================] - 6s 326us/step - loss: 0.0536 - acc: 0.9803\nEpoch 28/500\n17500/17500 [==============================] - 5s 312us/step - loss: 0.0502 - acc: 0.9813\nEpoch 29/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0491 - acc: 0.9823\nEpoch 30/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0485 - acc: 0.9818\nEpoch 31/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0461 - acc: 0.9831\nEpoch 32/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0423 - acc: 0.9842\nEpoch 33/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0476 - acc: 0.9832\nEpoch 34/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0431 - acc: 0.9836\nEpoch 35/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0428 - acc: 0.9842\nEpoch 36/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0466 - acc: 0.9827\nEpoch 37/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0439 - acc: 0.9844\nEpoch 38/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0403 - acc: 0.9850\nEpoch 39/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0407 - acc: 0.9847\nEpoch 40/500\n17500/17500 [==============================] - 5s 297us/step - loss: 0.0430 - acc: 0.9846\nEpoch 41/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0402 - acc: 0.9855\nEpoch 42/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0366 - acc: 0.9869\nEpoch 43/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0399 - acc: 0.9857\nEpoch 44/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0408 - acc: 0.9838\nEpoch 45/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0391 - acc: 0.9856\nEpoch 46/500\n17500/17500 [==============================] - 6s 319us/step - loss: 0.0378 - acc: 0.9862\nEpoch 47/500\n17500/17500 [==============================] - 5s 301us/step - loss: 0.0400 - acc: 0.9851\nEpoch 48/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0375 - acc: 0.9865\nEpoch 49/500\n17500/17500 [==============================] - 5s 293us/step - loss: 0.0368 - acc: 0.9874\nEpoch 50/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0420 - acc: 0.9849\nEpoch 51/500\n17500/17500 [==============================] - 5s 297us/step - loss: 0.0394 - acc: 0.9851\nEpoch 52/500\n17500/17500 [==============================] - 5s 297us/step - loss: 0.0363 - acc: 0.9873\nEpoch 53/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0351 - acc: 0.9863\nEpoch 54/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0323 - acc: 0.9881\nEpoch 55/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0395 - acc: 0.9861\nEpoch 56/500\n17500/17500 [==============================] - 5s 299us/step - loss: 0.0378 - acc: 0.9872\nEpoch 57/500\n17500/17500 [==============================] - 5s 297us/step - loss: 0.0376 - acc: 0.9866\nEpoch 58/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0332 - acc: 0.9883\nEpoch 59/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0351 - acc: 0.9881\nEpoch 60/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0341 - acc: 0.9888\nEpoch 61/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0381 - acc: 0.9850\nEpoch 62/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0372 - acc: 0.9881\nEpoch 63/500\n17500/17500 [==============================] - 5s 297us/step - loss: 0.0328 - acc: 0.9874\nEpoch 64/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0323 - acc: 0.9883\nEpoch 65/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0340 - acc: 0.9878\nEpoch 66/500\n17500/17500 [==============================] - 5s 297us/step - loss: 0.0295 - acc: 0.9891\nEpoch 67/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0342 - acc: 0.9878\nEpoch 68/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0318 - acc: 0.9885\nEpoch 69/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0345 - acc: 0.9873\nEpoch 70/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0301 - acc: 0.9895\nEpoch 71/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0321 - acc: 0.9880\nEpoch 72/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0295 - acc: 0.9894\nEpoch 73/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0318 - acc: 0.9883\nEpoch 74/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0308 - acc: 0.9890\nEpoch 75/500\n17500/17500 [==============================] - 5s 298us/step - loss: 0.0296 - acc: 0.9898\nEpoch 76/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0274 - acc: 0.9899\nEpoch 77/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0292 - acc: 0.9902\nEpoch 78/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0267 - acc: 0.9902\nEpoch 79/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0295 - acc: 0.9897\nEpoch 80/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0309 - acc: 0.9894\nEpoch 81/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0294 - acc: 0.9901\nEpoch 82/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0276 - acc: 0.9905\nEpoch 83/500\n17500/17500 [==============================] - 5s 299us/step - loss: 0.0308 - acc: 0.9891\nEpoch 84/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0263 - acc: 0.9903\nEpoch 85/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0292 - acc: 0.9899\nEpoch 86/500\n17500/17500 [==============================] - 5s 307us/step - loss: 0.0277 - acc: 0.9895\nEpoch 87/500\n17500/17500 [==============================] - 6s 325us/step - loss: 0.0271 - acc: 0.9901\nEpoch 88/500\n17500/17500 [==============================] - 5s 306us/step - loss: 0.0263 - acc: 0.9910\nEpoch 89/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0280 - acc: 0.9900\nEpoch 90/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0280 - acc: 0.9895\nEpoch 91/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0262 - acc: 0.9907\nEpoch 92/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0290 - acc: 0.9902\nEpoch 93/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0278 - acc: 0.9899\nEpoch 94/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0264 - acc: 0.9902\nEpoch 95/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0268 - acc: 0.9905\nEpoch 96/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0266 - acc: 0.9908\nEpoch 97/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0299 - acc: 0.9896\nEpoch 98/500\n17500/17500 [==============================] - 5s 298us/step - loss: 0.0242 - acc: 0.9924\nEpoch 99/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0305 - acc: 0.9905\nEpoch 100/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0224 - acc: 0.9920\nEpoch 101/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0268 - acc: 0.9901\nEpoch 102/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0269 - acc: 0.9899\nEpoch 103/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0261 - acc: 0.9916\nEpoch 104/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0245 - acc: 0.9914\nEpoch 105/500\n17500/17500 [==============================] - 6s 325us/step - loss: 0.0243 - acc: 0.9916\nEpoch 106/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0239 - acc: 0.9911\nEpoch 107/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0211 - acc: 0.9924\nEpoch 108/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0250 - acc: 0.9908\nEpoch 109/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0225 - acc: 0.9919\nEpoch 110/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0283 - acc: 0.9908\nEpoch 111/500\n17500/17500 [==============================] - 5s 293us/step - loss: 0.0264 - acc: 0.9906\nEpoch 112/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0244 - acc: 0.9921\nEpoch 113/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0251 - acc: 0.9917\nEpoch 114/500\n17500/17500 [==============================] - 5s 297us/step - loss: 0.0196 - acc: 0.9936\nEpoch 115/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0235 - acc: 0.9916\nEpoch 116/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0221 - acc: 0.9921\nEpoch 117/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0237 - acc: 0.9910\nEpoch 118/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0222 - acc: 0.9909\nEpoch 119/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0226 - acc: 0.9925\nEpoch 120/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0205 - acc: 0.9930\nEpoch 121/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0219 - acc: 0.9922\nEpoch 122/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0243 - acc: 0.9910\nEpoch 123/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0238 - acc: 0.9915\nEpoch 124/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0213 - acc: 0.9919\nEpoch 125/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0238 - acc: 0.9914\nEpoch 126/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0214 - acc: 0.9926\nEpoch 127/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0218 - acc: 0.9925\nEpoch 128/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0243 - acc: 0.9915\nEpoch 129/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0217 - acc: 0.9921\nEpoch 130/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0244 - acc: 0.9916\nEpoch 131/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0232 - acc: 0.9922\nEpoch 132/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0210 - acc: 0.9928\nEpoch 133/500\n17500/17500 [==============================] - 5s 296us/step - loss: 0.0210 - acc: 0.9925\nEpoch 134/500\n17500/17500 [==============================] - 5s 294us/step - loss: 0.0230 - acc: 0.9917\nEpoch 135/500\n17500/17500 [==============================] - 5s 295us/step - loss: 0.0209 - acc: 0.9927\nEpoch 136/500\n 1984/17500 [==>...........................] - ETA: 4s - loss: 0.0189 - acc: 0.9940\n```\n\nIn [31]:\n\n```py\nresnet_predictions = resnet_model.predict(X_test)\n\n```\n\n## MAKING SUBMISSIONS\n\n1.  Ensemble Model\n\nIn [32]:\n\n```py\n#making predictions\nprediction = np.hstack([p.reshape(-1,1) for p in prediction_scores.values()]) #taking the scores of all the trained models\npredictions_ensemble = np.mean(prediction, axis = 1)\nprint(predictions_ensemble.shape)\n\n```\n\n```\n(4000,)\n\n```\n\nIn [33]:\n\n```py\ndf_ensemble = pd.DataFrame(predictions_ensemble, columns = ['has_cactus'])\ndf_ensemble['has_cactus'] = df_ensemble['has_cactus'].apply(lambda x: 1 if x > 0.75 else 0)\n\n```\n\nIn [34]:\n\n```py\ndf_ensemble['id'] = ''\ncols = df_ensemble.columns.tolist()\ncols = cols[-1:] + cols[:-1]\ndf_ensemble = df_ensemble[cols]\n\nfor i, img in enumerate(test_images):\n    df_ensemble.set_value(i,'id',img)\n\n#making submission\ndf_ensemble.to_csv('ensemble_submission.csv',index = False)\n\n```\n\n```\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n  import sys\n\n```\n\n1.  VGG16\n\nIn [35]:\n\n```py\ndf_vgg = pd.DataFrame(predictions_vgg, columns = ['has_cactus'])\ndf_vgg['has_cactus'] = df_vgg['has_cactus'].apply(lambda x: 1 if x > 0.75 else 0)\n\n```\n\nIn [36]:\n\n```py\ndf_vgg['id'] = ''\ncols = df_vgg.columns.tolist()\ncols = cols[-1:] + cols[:-1]\ndf_vgg = df_vgg[cols]\n\nfor i, img in enumerate(test_images):\n    df_vgg.set_value(i,'id',img)\n\n#making submission\ndf_vgg.to_csv('vgg_submission.csv',index = False)\n\n```\n\n```\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n  import sys\n\n```\n\nIn [37]:\n\n```py\ndf_vgg.head()\n\n```\n\nOut[37]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | c662bde123f0f83b3caae0ffda237a93.jpg | 1 |\n| --- | --- | --- |\n| 1 | 9553eed7793d4cf88b5226d446d93dae.jpg | 0 |\n| --- | --- | --- |\n| 2 | 19f059a7ce41b25be1548bc4049b45ec.jpg | 1 |\n| --- | --- | --- |\n| 3 | fb4f464486f4894330273346ce939252.jpg | 1 |\n| --- | --- | --- |\n| 4 | b52558a522db6ec2501ae188b6d6e526.jpg | 1 |\n| --- | --- | --- |\n\n1.  Resnet50\n\nIn [38]:\n\n```py\ndf_resnet = pd.DataFrame(resnet_predictions, columns = ['has_cactus'])\ndf_resnet['has_cactus'] = df_resnet['has_cactus'].apply(lambda x: 1 if x > 0.75 else 0)\n\n```\n\nIn [39]:\n\n```py\ndf_resnet['id'] = ''\ncols = df_resnet.columns.tolist()\ncols = cols[-1:] + cols[:-1]\ndf_resnet = df_resnet[cols]\n\nfor i, img in enumerate(test_images):\n    df_resnet.set_value(i,'id',img)\n\n#making submission\ndf_resnet.to_csv('resnet_submission.csv',index = False)\n\n```\n\n```\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n  import sys\n\n```\n\n1.  Ensemble and VGG16\n\nIn [40]:\n\n```py\ndf_vgg1 = pd.DataFrame(predictions_vgg, columns = ['has_cactus'])\ndf_ensemble1 = pd.DataFrame(predictions_ensemble, columns = ['has_cactus'])\n\ndf_t = 0.5 * df_vgg1['has_cactus'] + 0.5 * df_ensemble1['has_cactus']\ndf_t = pd.DataFrame(df_t, columns = ['has_cactus'])\ndf_t['has_cactus'] = df_t['has_cactus'].apply(lambda x: 1 if x > 0.75 else 0)\n\ndf_t['id'] = ''\ncols = df_t.columns.tolist()\ncols = cols[-1:] + cols[:-1]\ndf_t = df_t[cols]\n\nfor i, img in enumerate(test_images):\n    df_t.set_value(i,'id',img)\n\n#making submission\ndf_t.to_csv('vgg_ensemble_submission.csv',index = False)\n\n```\n\n```\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:14: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n\n```\n\n1.  Ensemble, VGG16 and ResNet50\n\nIn [41]:\n\n```py\ndf_vgg2 = pd.DataFrame(predictions_vgg, columns = ['has_cactus'])\ndf_ensemble2 = pd.DataFrame(predictions_ensemble, columns = ['has_cactus'])\ndf_resnet2 = pd.DataFrame(resnet_predictions, columns = ['has_cactus'])\n\ndf_t2 = 0.45 * df_vgg2['has_cactus'] + 0.45 * df_ensemble2['has_cactus'] + 0.10 * df_resnet2['has_cactus']\ndf_t2 = pd.DataFrame(df_t2, columns = ['has_cactus'])\ndf_t2['has_cactus'] = df_t2['has_cactus'].apply(lambda x: 1 if x > 0.75 else 0)\n\ndf_t2['id'] = ''\ncols = df_t2.columns.tolist()\ncols = cols[-1:] + cols[:-1]\ndf_t2 = df_t2[cols]\n\nfor i, img in enumerate(test_images):\n    df_t2.set_value(i,'id',img)\n\n#making submission\ndf_t2.to_csv('vgg_ensemble_resnet_submission.csv',index = False)\n\n```\n\n```\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:15: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n  from ipykernel import kernelapp as app\n\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/cactus-identification-fastai-v1-0-46-ensemble.md",
    "content": "# Cactus Identification fastai v1.0.46 ensemble\n\n> Author: https://www.kaggle.com/mnpinto\n\n> From: https://www.kaggle.com/mnpinto/cactus-identification-fastai-v1-0-46-ensemble\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\n> Score: 0.9996\n\n# Cactus Identification fastai baseline\n\nIn [1]:\n\n```py\nimport fastai\nfrom fastai.vision import *\nfrom sklearn.model_selection import KFold\n\n```\n\nIn [2]:\n\n```py\n# Copy pretrained model weights to the default path\n!mkdir '/tmp/.torch'\n!mkdir '/tmp/.torch/models/'\n#!cp '../input/resnet18/resnet18.pth' '/tmp/.torch/models/resnet18-5c106cde.pth'\n#!cp '../input/densenet121/densenet121.pth' '/tmp/.torch/models/densenet121-a639ec97.pth'\n!cp '../input/densenet201/densenet201.pth' '/tmp/.torch/models/densenet201-c1103571.pth'\n\n```\n\nIn [3]:\n\n```py\nfastai.__version__\n\n```\n\nOut[3]:\n\n```\n'1.0.46'\n```\n\nIn [4]:\n\n```py\ndata_path = Path('../input/aerial-cactus-identification')\ndf = pd.read_csv(data_path/'train.csv')\ndf.head()\n\n```\n\nOut[4]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |\n| --- | --- | --- |\n| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |\n| --- | --- | --- |\n| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |\n| --- | --- | --- |\n| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |\n| --- | --- | --- |\n| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |\n| --- | --- | --- |\n\nIn [5]:\n\n```py\nsub_csv = pd.read_csv(data_path/'sample_submission.csv')\nsub_csv.head()\n\n```\n\nOut[5]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 000940378805c44108d287872b2f04ce.jpg | 0.5 |\n| --- | --- | --- |\n| 1 | 0017242f54ececa4512b4d7937d1e21e.jpg | 0.5 |\n| --- | --- | --- |\n| 2 | 001ee6d8564003107853118ab87df407.jpg | 0.5 |\n| --- | --- | --- |\n| 3 | 002e175c3c1e060769475f52182583d0.jpg | 0.5 |\n| --- | --- | --- |\n| 4 | 0036e44a7e8f7218e9bc7bf8137e4943.jpg | 0.5 |\n| --- | --- | --- |\n\nIn [6]:\n\n```py\ndef create_databunch(valid_idx):\n    test = ImageList.from_df(sub_csv, path=data_path/'test', folder='test')\n    data = (ImageList.from_df(df, path=data_path/'train', folder='train')\n            .split_by_idx(valid_idx)\n            .label_from_df()\n            .add_test(test)\n            .transform(get_transforms(flip_vert=True, max_rotate=20.0), size=128)\n            .databunch(path='.', bs=64)\n            .normalize(imagenet_stats)\n           )\n    return data\n\n```\n\n**5 fold ensemble**\n\nIn [7]:\n\n```py\nkf = KFold(n_splits=5, random_state=379)\nepochs = 6\nlr = 1e-2\npreds = []\nfor train_idx, valid_idx in kf.split(df):\n    data = create_databunch(valid_idx)\n    learn = create_cnn(data, models.densenet201, metrics=[accuracy])\n    learn.fit_one_cycle(epochs, slice(lr))\n    learn.unfreeze()\n    learn.fit_one_cycle(epochs, slice(lr/400, lr/4))\n    learn.fit_one_cycle(epochs, slice(lr/800, lr/8))\n    preds.append(learn.get_preds(ds_type=DatasetType.Test))\n\n```\n\nTotal time: 06:07\n\n| epoch | train_loss | valid_loss | accuracy | time |\n| --- | --- | --- | --- | --- |\n| 1 | 0.056767 | 0.018588 | 0.994000 | 01:08 |\n| 2 | 0.022490 | 0.010378 | 0.995429 | 01:00 |\n| 3 | 0.025892 | 0.003561 | 0.998857 | 01:00 |\n| 4 | 0.012079 | 0.038481 | 0.996571 | 00:59 |\n| 5 | 0.006535 | 0.002324 | 0.999143 | 00:57 |\n| 6 | 0.003622 | 0.002164 | 0.999143 | 01:01 |\n\n 66.67% [4/6 04:47<02:23]\n\n| epoch | train_loss | valid_loss | accuracy | time |\n| --- | --- | --- | --- | --- |\n| 1 | 0.007291 | 0.003572 | 0.998286 | 01:12 |\n| 2 | 0.014519 | 0.005793 | 0.998286 | 01:11 |\n| 3 | 0.008674 | 0.003707 | 0.998286 | 01:10 |\n| 4 | 0.007575 | 0.001763 | 0.999143 | 01:13 |\n\n 91.28% [199/218 00:58<00:05 0.0037]In [8]:\n\n```py\nens = torch.cat([preds[i][0][:,1].view(-1, 1) for i in range(5)], dim=1)\nens  = (ens.mean(1)>0.5).long(); ens[:10]\n\n```\n\nOut[8]:\n\n```\ntensor([1, 1, 0, 0, 1, 1, 1, 1, 1, 0])\n```\n\nIn [9]:\n\n```py\nsub_csv['has_cactus'] = ens\n\n```\n\nIn [10]:\n\n```py\nsub_csv.to_csv('submission.csv', index=False)\n\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/cactus-identification-with-pytorch.md",
    "content": "# Cactus Identification with Pytorch\n\n> Author: https://www.kaggle.com/nelsongriffiths\n\n> From: https://www.kaggle.com/nelsongriffiths/cactus-identification-with-pytorch\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\nIn [1]:\n\n```py\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom sklearn.model_selection import train_test_split\nimport os\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision import datasets\nimport torchvision.transforms as transforms\nfrom torch.utils.data.sampler import SubsetRandomSampler\n\nimport cv2\nimport matplotlib.pyplot as plt\n\ntrain_on_gpu = torch.cuda.is_available()\n\nif not train_on_gpu:\n    print('CUDA is not available.  Training on CPU ...')\nelse:\n    print('CUDA is available!  Training on GPU ...')\n\n```\n\n```\nCUDA is available!  Training on GPU ...\n\n```\n\n# Data Prep\n\nFirst, I am going to import our data sources and take a look at what we are working with. We have a csv file that contains our target variable and a folder with our cactus images.\n\nIn [2]:\n\n```py\ndf = pd.read_csv('../input/train.csv')\ndf.head()\n\n```\n\nOut[2]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |\n| --- | --- | --- |\n| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |\n| --- | --- | --- |\n| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |\n| --- | --- | --- |\n| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |\n| --- | --- | --- |\n| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |\n| --- | --- | --- |\n\nIn [3]:\n\n```py\ndf['has_cactus'].value_counts(normalize=True)\n\n```\n\nOut[3]:\n\n```\n1    0.750629\n0    0.249371\nName: has_cactus, dtype: float64\n```\n\nIn [4]:\n\n```py\ntrain_df, val_df = train_test_split(df, stratify = df.has_cactus, test_size=.2)\n\n```\n\nIn [5]:\n\n```py\n#Checking that validation set has same proportions as original training data\nval_df['has_cactus'].value_counts(normalize=True)\n\n```\n\nOut[5]:\n\n```\n1    0.750571\n0    0.249429\nName: has_cactus, dtype: float64\n```\n\nIn [6]:\n\n```py\n#Build a class for our data to put our images and target variables into our pytorch dataloader\n# https://stanford.edu/~shervine/blog/pytorch-how-to-generate-data-parallel\n\nclass DataSet(torch.utils.data.Dataset):\n    def __init__(self, labels, data_directory, transform=None):\n        super().__init__()\n        self.labels = labels.values\n        self.data_dir = data_directory\n        self.transform=transform\n\n    def __len__(self):\n        return len(self.labels)\n\n    def __getitem__(self, index):\n        name, label = self.labels[index]\n        img_path = os.path.join(self.data_dir, name)\n        img = cv2.imread(img_path)\n\n        if self.transform is not None:\n            img = self.transform(img)\n        return img, label\n\n```\n\nIn [7]:\n\n```py\nbatch_size = 32\n\n# Transform training data with random flips and normalize it to prepare it for dataloader\ntrain_transforms = transforms.Compose([transforms.ToPILImage(),\n                                       transforms.RandomHorizontalFlip(),\n                                       transforms.ToTensor(),\n                                      transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])\n\nval_transforms = transforms.Compose([transforms.ToPILImage(),\n                                     transforms.ToTensor(),\n                                     transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])\n\ntrain_data = DataSet(train_df,'../input/train/train', transform = train_transforms)\nval_data = DataSet(val_df,'../input/train/train', transform = val_transforms)\n\ntrain_data_loader = torch.utils.data.DataLoader(train_data, batch_size = batch_size, shuffle = True)\nval_data_loader = torch.utils.data.DataLoader(train_data, batch_size = batch_size, shuffle = True)\n\n```\n\nIn [8]:\n\n```py\n#Checking what our cactus look like\nfig,ax = plt.subplots(1,3,figsize=(15,5))\n\nfor i, idx in enumerate(train_df[train_df['has_cactus']==1]['id'][0:3]):\n  path = os.path.join('../input/train/train',idx)\n  ax[i].imshow(cv2.imread(path))\n\n```\n\n![](cactus-identification-with-pytorch_files/__results___8_0.png)In [9]:\n\n```py\n#Building a CNN from scratch\n\nclass Net(nn.Module):\n    def __init__(self):\n        super().__init__()\n\n        self.conv1 = nn.Conv2d(3, 16, 3, padding = 1)\n        self.conv2 = nn.Conv2d(16, 32, 3, padding = 1)\n        self.conv3 = nn.Conv2d(32, 64, 3, padding = 1)\n        self.conv4 = nn.Conv2d(64, 128, 3, padding = 1)\n        self.pool = nn.MaxPool2d(2, 2)\n        self.fc1 = nn.Linear(2*16*16, 256)\n        self.fc2 = nn.Linear(256, 128)\n        self.fc3 = nn.Linear(128, 64)\n        self.fc4 = nn.Linear(64, 2)\n        self.dropout = nn.Dropout(p = .25)\n\n    def forward(self, x):\n\n        x = self.pool(F.relu(self.conv1(x)))\n        x = self.pool(F.relu(self.conv2(x)))\n        x = self.pool(F.relu(self.conv3(x)))\n        x = self.pool(F.relu(self.conv4(x)))\n\n        x = x.view(-1, 2*16*16)\n        x = self.dropout(x)\n        x = F.relu(self.fc1(x))\n        x = self.dropout(x)\n        x = F.relu(self.fc2(x))\n        x = self.dropout(x)\n        x = F.relu(self.fc3(x))\n        x = self.dropout(x)\n        x = F.relu(self.fc4(x))\n\n        return x\n\n```\n\nIn [10]:\n\n```py\nmodel = Net()\nif train_on_gpu:\n    model = model.cuda()\n\nepochs = 30\nlearning_rate = .003\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adamax(model.parameters(), lr=learning_rate)\n\n```\n\nIn [11]:\n\n```py\n#Training and validation for model\n\nbest_loss = np.Inf\nbest_model = Net()\nif train_on_gpu:\n    best_model.cuda()\n\nfor epoch in range(1, epochs+1):\n    train_loss = 0\n    val_loss = 0\n\n    model.train()\n    for images, labels in train_data_loader:\n\n        if train_on_gpu:\n            images = images.cuda()\n            labels = labels.cuda()\n\n        optimizer.zero_grad()\n        out = model(images)\n        loss = criterion(out, labels)\n\n        loss.backward()\n        optimizer.step()\n\n        train_loss += loss.item()\n        #print('Loss: {}'.format(loss.item()))\n\n    model.eval()\n    for images, labels in val_data_loader:\n\n        if train_on_gpu:\n            images = images.cuda()\n            labels = labels.cuda()\n\n        out = model(images)\n        loss = criterion(out, labels)\n\n        val_loss += loss.item()\n\n    train_loss = train_loss/len(train_data_loader.dataset)\n    val_loss = val_loss/len(val_data_loader.dataset)  \n    print('Epoch: {}  \\tTraining Loss: {:.6f}  \\tValidation Loss: {:.6f}'.format(epoch, train_loss, val_loss))\n\n    #Saving the weights of the best model according to validation score\n    if val_loss < best_loss:\n        print('Improved Model Score - Updating Best Model Parameters...')\n        best_model.load_state_dict(model.state_dict())\n\n```\n\n```\nEpoch: 1 \tTraining Loss: 0.007002 \tValidation Loss: 0.003353\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 2 \tTraining Loss: 0.003053 \tValidation Loss: 0.002155\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 3 \tTraining Loss: 0.002027 \tValidation Loss: 0.001604\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 4 \tTraining Loss: 0.001602 \tValidation Loss: 0.001545\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 5 \tTraining Loss: 0.001286 \tValidation Loss: 0.001005\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 6 \tTraining Loss: 0.001038 \tValidation Loss: 0.001039\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 7 \tTraining Loss: 0.000975 \tValidation Loss: 0.001050\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 8 \tTraining Loss: 0.000826 \tValidation Loss: 0.000844\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 9 \tTraining Loss: 0.000768 \tValidation Loss: 0.000574\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 10 \tTraining Loss: 0.000666 \tValidation Loss: 0.000445\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 11 \tTraining Loss: 0.000604 \tValidation Loss: 0.000394\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 12 \tTraining Loss: 0.000607 \tValidation Loss: 0.000716\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 13 \tTraining Loss: 0.000459 \tValidation Loss: 0.000416\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 14 \tTraining Loss: 0.000544 \tValidation Loss: 0.000305\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 15 \tTraining Loss: 0.000390 \tValidation Loss: 0.000316\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 16 \tTraining Loss: 0.000345 \tValidation Loss: 0.000183\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 17 \tTraining Loss: 0.000329 \tValidation Loss: 0.000158\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 18 \tTraining Loss: 0.000285 \tValidation Loss: 0.000158\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 19 \tTraining Loss: 0.000244 \tValidation Loss: 0.000087\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 20 \tTraining Loss: 0.000260 \tValidation Loss: 0.000235\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 21 \tTraining Loss: 0.000208 \tValidation Loss: 0.000061\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 22 \tTraining Loss: 0.000214 \tValidation Loss: 0.000726\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 23 \tTraining Loss: 0.000208 \tValidation Loss: 0.000097\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 24 \tTraining Loss: 0.000172 \tValidation Loss: 0.000169\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 25 \tTraining Loss: 0.000192 \tValidation Loss: 0.000245\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 26 \tTraining Loss: 0.000222 \tValidation Loss: 0.000091\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 27 \tTraining Loss: 0.000112 \tValidation Loss: 0.000061\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 28 \tTraining Loss: 0.000119 \tValidation Loss: 0.000155\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 29 \tTraining Loss: 0.000139 \tValidation Loss: 0.000022\nImproved Model Score - Updating Best Model Parameters...\nEpoch: 30 \tTraining Loss: 0.000132 \tValidation Loss: 0.000035\nImproved Model Score - Updating Best Model Parameters...\n\n```\n\nIn [12]:\n\n```py\n#Check model accuracy\nbest_model.eval()\nwith torch.no_grad():\n    correct = 0\n    total = 0\n    for images, labels in val_data_loader:\n        if train_on_gpu:\n            images = images.cuda()\n            labels = labels.cuda()\n        outputs = best_model(images)\n        _, predicted = torch.max(outputs.data, 1)\n        total += labels.size(0)\n        correct += (predicted == labels).sum().item()\n\n    print('Test Accuracy: {} %'.format(100 * correct / total))\n\n```\n\n```\nTest Accuracy: 99.96428571428571 %\n\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/cnn-model-with-keras.md",
    "content": "# Simple CNN Model with Keras\n\n> Author: https://www.kaggle.com/frules11\n\n> From: https://www.kaggle.com/frules11/cnn-model-with-keras\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\n> Score: 1.0000\n\nIn [1]:\n\n```py\n# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.\n\n```\n\n```\n['test', 'train', 'train.csv', 'sample_submission.csv']\n\n```\n\nIn [2]:\n\n```py\nimport os\nimport cv2\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport tensorflow.contrib.slim as slim\n\nfrom tqdm import tqdm\nfrom keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization, Dropout\nfrom keras.models import Sequential\nfrom keras.optimizers import Adam\n\n```\n\n```\nUsing TensorFlow backend.\n\n```\n\nIn [3]:\n\n```py\nclass DataLoader:\n    def __init__(self, npy_file: str = \"npy_data\"):\n        self.npy_file = npy_file\n        self.csv_name = \"../input/train.csv\"\n        self.df = self.read_csv()\n        self.n_classes = 2\n\n        os.makedirs(self.npy_file, exist_ok=True)\n\n    def read_csv(self):\n        df = pd.read_csv(self.csv_name)\n\n        return df\n\n    def read_data(self, load_from_npy: bool = True, size2resize: tuple = (75, 75), make_gray: bool = True,\n                  save: bool = True, categorical: bool = False, n_classes: int = 2):\n\n        x_data = []\n        y_data = []\n\n        if load_from_npy:\n            try:\n                x_data = np.load(fr\"{self.npy_file}/x_data.npy\")\n                y_data = np.load(fr\"{self.npy_file}/y_data.npy\")\n            except FileNotFoundError:\n                load_from_npy = False\n                print(\"NPY files not found!\")\n                pass\n\n        if not load_from_npy:\n            x_data = []\n            y_data = []\n\n            for dir_label in tqdm(self.df.values):\n                img = cv2.imread(os.path.join(\"../input\", \"train/train\", dir_label[0]))\n\n                if make_gray:\n                    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n                img = cv2.resize(img, size2resize)\n\n                x_data.append(img)\n                y_data.append(int(dir_label[1]))\n\n                del img\n\n            x_data = np.array(x_data)\n            y_data = np.array(y_data)\n\n            if save:\n                np.save(fr\"{self.npy_file}/x_data.npy\", x_data)\n                np.save(fr\"{self.npy_file}/y_data.npy\", y_data)\n\n        if categorical:\n            y_data = tf.keras.utils.to_categorical(y_data, num_classes=n_classes)\n\n        if not categorical:\n            y_data = y_data.reshape(-1, 1)\n\n        if load_from_npy and make_gray:\n            try:\n                x_data_2 = [cv2.cvtColor(n, cv2.COLOR_BGR2GRAY) for n in x_data]\n                x_data = x_data_2\n            except cv2.error:\n                pass\n\n        if make_gray:\n            x_data = np.expand_dims(x_data, axis=-1)\n\n        return x_data, y_data\n\n    def read_test_data(self, load_from_npy: bool = True, size2resize: tuple = (75, 75), make_gray: bool = True,\n                  save: bool = True, categorical: bool = False, n_classes: int = 2):\n\n        test_df = pd.read_csv(\"../input/sample_submission.csv\")\n\n        x_data = []\n        y_data = []\n\n        if load_from_npy:\n            try:\n                x_data = np.load(fr\"{self.npy_file}/x_data_test.npy\")\n                y_data = np.load(fr\"{self.npy_file}/y_data_test.npy\")\n            except FileNotFoundError:\n                load_from_npy = False\n                print(\"NPY files not found!\")\n                pass\n\n        if not load_from_npy:\n            x_data = []\n            y_data = []\n\n            for dir_label in tqdm(test_df.values):\n                img = cv2.imread(os.path.join(\"../input\", \"test/test\", dir_label[0]))\n\n                if make_gray:\n                    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n                img = cv2.resize(img, size2resize)\n\n                x_data.append(img)\n                y_data.append(int(dir_label[1]))\n\n                del img\n\n            x_data = np.array(x_data)\n            y_data = np.array(y_data)\n\n            if save:\n                np.save(fr\"{self.npy_file}/x_data_test.npy\", x_data)\n                np.save(fr\"{self.npy_file}/y_data_test.npy\", y_data)\n\n        if categorical:\n            y_data = tf.keras.utils.to_categorical(y_data, num_classes=n_classes)\n\n        if not categorical:\n            y_data = y_data.reshape(-1, 1)\n\n        if load_from_npy and make_gray:\n            try:\n                x_data_2 = [cv2.cvtColor(n, cv2.COLOR_BGR2GRAY) for n in x_data]\n                x_data = x_data_2\n            except cv2.error:\n                pass\n\n        if make_gray:\n            x_data = np.expand_dims(x_data, axis=-1)\n\n        return x_data, y_data\n\n```\n\nIn [4]:\n\n```py\nclass TrainWithKeras:\n    def __init__(self, x_data, y_data, lr: float = 0.001, epochs: int = 10, batch_size: int = 32,\n                 loss: str = \"categorical_crossentropy\", model_path: str = \"model.h5\"):\n        self.x_data = x_data\n        self.y_data = y_data\n        self.model_path = model_path\n\n        self.epochs = epochs\n        self.batch_size = batch_size\n\n        self.optimizer = Adam(lr=lr)\n        self.loss = loss\n\n    def make_model(self, summarize: bool = True):\n        model = Sequential()\n\n        model.add(Conv2D(64, (3, 3), strides=1, activation=\"relu\",\n                         input_shape=(self.x_data.shape[1], self.x_data.shape[2], self.x_data.shape[3])))\n        model.add(MaxPooling2D())\n        model.add(Conv2D(128, (3, 3), strides=1, activation=\"relu\"))\n        model.add(Dropout(0.3))\n        model.add(BatchNormalization())\n\n        model.add(Conv2D(256, (3, 3), strides=1, activation=\"relu\"))\n        model.add(MaxPooling2D())\n        model.add(Conv2D(512, (3, 3), strides=1, activation=\"relu\"))\n        model.add(Dropout(0.3))\n\n        model.add(Conv2D(1024, (3, 3), strides=1, activation=\"relu\"))\n\n        model.add(Flatten())\n\n        model.add(Dense(1024, activation=\"relu\"))\n        model.add(Dropout(0.3))\n        model.add(Dense(2, activation=\"softmax\"))\n\n        if summarize:\n            model.summary()\n\n        return model\n\n    def compile(self, kmodel: Sequential):\n        kmodel.compile(loss=self.loss, optimizer=self.optimizer, metrics=[\"acc\"])\n\n        return kmodel\n\n    def train(self, kmodel: Sequential, save: bool = True):\n        history = kmodel.fit(self.x_data, self.y_data, batch_size=self.batch_size, epochs=self.epochs,\n                             validation_split=0.0)\n\n        if save:\n            kmodel.save(self.model_path)\n\n        return history, kmodel\n\n```\n\nIn [5]:\n\n```py\nclass MakeSubmission:\n    def __init__(self, x_test: np.array, model_path: str, csv_path: str):\n        self.x_test = x_test\n        self.model_path = model_path\n        self.csv_path = csv_path\n\n        self.model = tf.keras.models.load_model(self.model_path)\n        self.df = pd.read_csv(self.csv_path)\n\n        preds = self.make_predictions()\n\n        submission = pd.DataFrame({'id': self.df['id'], 'has_cactus': preds})\n        submission.to_csv(\"sample_submission.csv\", index=False)\n\n    def make_predictions(self, make_it_ready: bool = True):\n        preds = self.model.predict(self.x_test)\n\n        if make_it_ready:\n            preds = [np.argmax(n) for n in preds]\n\n        return preds\n\n```\n\nIn [6]:\n\n```py\nos.makedirs(\"models\", exist_ok=True)\n\ndl = DataLoader()\nX_data, Y_data = dl.read_data(True, (32, 32), False, True, True, 2)\n\n```\n\n```\n  0%|          | 71/17500 [00:00<00:24, 703.67it/s]\n```\n\n```\nNPY files not found!\n\n```\n\n```\n100%|██████████| 17500/17500 [00:25<00:00, 692.41it/s]\n\n```\n\nIn [7]:\n\n```py\ntrainer = TrainWithKeras(X_data, Y_data, model_path=\"models/model.h5\", epochs=50, batch_size=1024, lr=0.0002)\nmodel = trainer.make_model()\nmodel = trainer.compile(model)\n\nhistroy = trainer.train(model)\n\n```\n\n```\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_1 (Conv2D)            (None, 30, 30, 64)        1792      \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 15, 15, 64)        0         \n_________________________________________________________________\nconv2d_2 (Conv2D)            (None, 13, 13, 128)       73856     \n_________________________________________________________________\ndropout_1 (Dropout)          (None, 13, 13, 128)       0         \n_________________________________________________________________\nbatch_normalization_1 (Batch (None, 13, 13, 128)       512       \n_________________________________________________________________\nconv2d_3 (Conv2D)            (None, 11, 11, 256)       295168    \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 5, 5, 256)         0         \n_________________________________________________________________\nconv2d_4 (Conv2D)            (None, 3, 3, 512)         1180160   \n_________________________________________________________________\ndropout_2 (Dropout)          (None, 3, 3, 512)         0         \n_________________________________________________________________\nconv2d_5 (Conv2D)            (None, 1, 1, 1024)        4719616   \n_________________________________________________________________\nflatten_1 (Flatten)          (None, 1024)              0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 1024)              1049600   \n_________________________________________________________________\ndropout_3 (Dropout)          (None, 1024)              0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 2)                 2050      \n=================================================================\nTotal params: 7,322,754\nTrainable params: 7,322,498\nNon-trainable params: 256\n_________________________________________________________________\nEpoch 1/50\n17500/17500 [==============================] - 5s 291us/step - loss: 0.4455 - acc: 0.7814\nEpoch 2/50\n17500/17500 [==============================] - 2s 88us/step - loss: 0.1582 - acc: 0.9434\nEpoch 3/50\n17500/17500 [==============================] - 2s 88us/step - loss: 0.0925 - acc: 0.9665\nEpoch 4/50\n17500/17500 [==============================] - 2s 88us/step - loss: 0.0686 - acc: 0.9747\nEpoch 5/50\n17500/17500 [==============================] - 2s 88us/step - loss: 0.0623 - acc: 0.9774\nEpoch 6/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0475 - acc: 0.9833\nEpoch 7/50\n17500/17500 [==============================] - 2s 88us/step - loss: 0.0484 - acc: 0.9819\nEpoch 8/50\n17500/17500 [==============================] - 2s 88us/step - loss: 0.0393 - acc: 0.9862\nEpoch 9/50\n17500/17500 [==============================] - 2s 89us/step - loss: 0.0394 - acc: 0.9858\nEpoch 10/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0325 - acc: 0.9884\nEpoch 11/50\n17500/17500 [==============================] - 2s 86us/step - loss: 0.0468 - acc: 0.9848\nEpoch 12/50\n17500/17500 [==============================] - 1s 85us/step - loss: 0.0314 - acc: 0.9895\nEpoch 13/50\n17500/17500 [==============================] - 1s 86us/step - loss: 0.0414 - acc: 0.9859\nEpoch 14/50\n17500/17500 [==============================] - 1s 85us/step - loss: 0.0295 - acc: 0.9893\nEpoch 15/50\n17500/17500 [==============================] - 1s 86us/step - loss: 0.0203 - acc: 0.9929\nEpoch 16/50\n17500/17500 [==============================] - 2s 86us/step - loss: 0.0251 - acc: 0.9920\nEpoch 17/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0193 - acc: 0.9935\nEpoch 18/50\n17500/17500 [==============================] - 2s 86us/step - loss: 0.0247 - acc: 0.9914\nEpoch 19/50\n17500/17500 [==============================] - 2s 86us/step - loss: 0.0179 - acc: 0.9938\nEpoch 20/50\n17500/17500 [==============================] - 2s 86us/step - loss: 0.0149 - acc: 0.9946\nEpoch 21/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0119 - acc: 0.9957\nEpoch 22/50\n17500/17500 [==============================] - 2s 86us/step - loss: 0.0110 - acc: 0.9963\nEpoch 23/50\n17500/17500 [==============================] - 1s 85us/step - loss: 0.0120 - acc: 0.9958\nEpoch 24/50\n17500/17500 [==============================] - 2s 86us/step - loss: 0.0228 - acc: 0.9921\nEpoch 25/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0134 - acc: 0.9960\nEpoch 26/50\n17500/17500 [==============================] - 2s 88us/step - loss: 0.0104 - acc: 0.9963\nEpoch 27/50\n17500/17500 [==============================] - 2s 86us/step - loss: 0.0136 - acc: 0.9954\nEpoch 28/50\n17500/17500 [==============================] - 1s 85us/step - loss: 0.0086 - acc: 0.9970\nEpoch 29/50\n17500/17500 [==============================] - 2s 86us/step - loss: 0.0096 - acc: 0.9967\nEpoch 30/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0080 - acc: 0.9971\nEpoch 31/50\n17500/17500 [==============================] - 1s 86us/step - loss: 0.0204 - acc: 0.9934\nEpoch 32/50\n17500/17500 [==============================] - 1s 86us/step - loss: 0.0141 - acc: 0.9948\nEpoch 33/50\n17500/17500 [==============================] - 2s 86us/step - loss: 0.0067 - acc: 0.9974\nEpoch 34/50\n17500/17500 [==============================] - 1s 85us/step - loss: 0.0070 - acc: 0.9975\nEpoch 35/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0129 - acc: 0.9950\nEpoch 36/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0072 - acc: 0.9976\nEpoch 37/50\n17500/17500 [==============================] - 2s 88us/step - loss: 0.0204 - acc: 0.9926\nEpoch 38/50\n17500/17500 [==============================] - 2s 88us/step - loss: 0.0097 - acc: 0.9964\nEpoch 39/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0054 - acc: 0.9983\nEpoch 40/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0055 - acc: 0.9979\nEpoch 41/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0059 - acc: 0.9978\nEpoch 42/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0095 - acc: 0.9965\nEpoch 43/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0057 - acc: 0.9979\nEpoch 44/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0058 - acc: 0.9983\nEpoch 45/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0055 - acc: 0.9982\nEpoch 46/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0036 - acc: 0.9986\nEpoch 47/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0049 - acc: 0.9983\nEpoch 48/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0055 - acc: 0.9981\nEpoch 49/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0044 - acc: 0.9984\nEpoch 50/50\n17500/17500 [==============================] - 2s 87us/step - loss: 0.0031 - acc: 0.9989\n\n```\n\nIn [8]:\n\n```py\nX_data_test, Y_data_test = dl.read_test_data(True, (32, 32), False, True, False)\nms = MakeSubmission(X_data_test, \"models/model.h5\", \"../input/sample_submission.csv\")\n\n```\n\n```\n  2%|▏         | 62/4000 [00:00<00:06, 614.87it/s]\n```\n\n```\nNPY files not found!\n\n```\n\n```\n100%|██████████| 4000/4000 [00:05<00:00, 669.71it/s]\n\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/data-augmentation-vgg16-cnn.md",
    "content": "# Data Augmentation + VGG16 + CNN\n\n> Author: https://www.kaggle.com/alperkoc\n\n> From: https://www.kaggle.com/alperkoc/data-augmentation-vgg16-cnn\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\n> Score: 0.9995\n\n## Import Packages\n\nIn [1]:\n\n```py\nimport cv2\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nfrom tqdm import tqdm, tqdm_notebook\nfrom keras.models import Sequential\nimport keras\nfrom keras.layers import Activation, Dropout, Flatten, Dense,Conv2D,Conv3D,MaxPooling2D,AveragePooling2D,BatchNormalization\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import confusion_matrix,classification_report,roc_auc_score\nimport seaborn as sns\nimport tensorflow as tf\nimport matplotlib.image as mpimg\nprint(os.listdir(\"../input\"))\nprint(os.listdir(\"../input/weights/\"))\nIMAGE_SIZE = 32\n\n```\n\n```\nUsing TensorFlow backend.\n\n```\n\n```\n['aerial-cactus-identification', 'weights']\n['vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5']\n\n```\n\n## Set Directories\n\nIn [2]:\n\n```py\ntrain_dir = \"../input/aerial-cactus-identification/train/train/\"\ntest_dir = \"../input/aerial-cactus-identification/test/test/\"\ntrain_df = pd.read_csv('../input/aerial-cactus-identification/train.csv')\ntrain_df.head()\n\n```\n\nOut[2]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |\n| --- | --- | --- |\n| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |\n| --- | --- | --- |\n| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |\n| --- | --- | --- |\n| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |\n| --- | --- | --- |\n| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |\n| --- | --- | --- |\n\n## Check out an image sample\n\nIn [3]:\n\n```py\nim = cv2.imread(\"../input/aerial-cactus-identification/train/train/01e30c0ba6e91343a12d2126fcafc0dd.jpg\")\nplt.imshow(im)\n\n```\n\nOut[3]:\n\n```\n<matplotlib.image.AxesImage at 0x7fa15e5ab208>\n```\n\n![](data-augmentation-vgg16-cnn_files/__results___5_1.png)\n\n## Read and convert horizontally an vertically augmented images to numpy array\n\nIn [4]:\n\n```py\nX_tr = []\nY_tr = []\nimges = train_df['id'].values\nfor img_id in tqdm_notebook(imges):\n    image = np.array(cv2.imread(train_dir + img_id))\n    X_tr.append(image)\n    Y_tr.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])  \n\n    X_tr.append(np.flip(image))\n    Y_tr.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])  \n\n    X_tr.append(np.flipud(image))\n    Y_tr.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])  \n\n    X_tr.append(np.fliplr(image))\n    Y_tr.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])  \n\nX_tr = np.asarray(X_tr)\nX_tr = X_tr.astype('float32')\nX_tr /= 255\nY_tr = np.asarray(Y_tr)\n\n```\n\n# Save an instance of initial images for future use\n\nIn [5]:\n\n```py\nX_tr_2 = X_tr\nY_tr_2 = Y_tr\n\n```\n\nIn [6]:\n\n```py\nX_tr = X_tr_2\nY_tr = Y_tr_2\n\n```\n\nIn [7]:\n\n```py\nX_tr.shape,Y_tr.shape\n\n```\n\nOut[7]:\n\n```\n((70000, 32, 32, 3), (70000,))\n```\n\n# Read test images\n\nIn [8]:\n\n```py\ntest_image_names = []\nfor filename in os.listdir(test_dir):\n    test_image_names.append(filename)\ntest_image_names.sort()\nX_ts = []\n#imges = test_df['id'].values\nfor img_id in tqdm_notebook(test_image_names):\n    X_ts.append(cv2.imread(test_dir + img_id))    \nX_ts = np.asarray(X_ts)\nX_ts = X_ts.astype('float32')\nX_ts /= 255\n\n```\n\nIn [9]:\n\n```py\nx_train,x_test,y_train,y_test = train_test_split(X_tr, Y_tr, test_size = 0.2 , stratify = Y_tr )\n\n```\n\n# Load weights of pretrained VGG16 model\n\nIn [10]:\n\n```py\nbase=keras.applications.vgg16.VGG16(include_top=False, weights='../input/weights/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',input_shape=(32,32,3))\n\n```\n\n# Train\n\nIn [11]:\n\n```py\nprint(\"Current train size:\",X_tr.shape)\nmodel = Sequential()\nmodel.add(base)\n\nmodel.add(Flatten())\nmodel.add(Dense(256, use_bias=True))\nmodel.add(BatchNormalization())\nmodel.add(Activation(\"relu\"))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(256,activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Dense(16, activation='tanh'))\nmodel.add(Dense(1, activation='sigmoid'))\n\nmodel.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])\nmodel.summary()\ncallback=[keras.callbacks.EarlyStopping(monitor='val_acc', patience=20, verbose=1, mode='auto', restore_best_weights=True),\n         keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=1, mode='auto')]\nmodel.fit(X_tr,Y_tr,batch_size=64, epochs=80, verbose=1,   validation_split=0.1,callbacks=callback)\n\n```\n\n```\nCurrent train size: (70000, 32, 32, 3)\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nvgg16 (Model)                (None, 1, 1, 512)         14714688  \n_________________________________________________________________\nflatten_1 (Flatten)          (None, 512)               0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 256)               131328    \n_________________________________________________________________\nbatch_normalization_1 (Batch (None, 256)               1024      \n_________________________________________________________________\nactivation_1 (Activation)    (None, 256)               0         \n_________________________________________________________________\ndropout_1 (Dropout)          (None, 256)               0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 256)               65792     \n_________________________________________________________________\nbatch_normalization_2 (Batch (None, 256)               1024      \n_________________________________________________________________\ndense_3 (Dense)              (None, 16)                4112      \n_________________________________________________________________\ndense_4 (Dense)              (None, 1)                 17        \n=================================================================\nTotal params: 14,917,985\nTrainable params: 14,916,961\nNon-trainable params: 1,024\n_________________________________________________________________\nTrain on 63000 samples, validate on 7000 samples\nEpoch 1/80\n63000/63000 [==============================] - 33s 519us/step - loss: 0.0982 - acc: 0.9667 - val_loss: 0.1541 - val_acc: 0.9523\nEpoch 2/80\n63000/63000 [==============================] - 30s 472us/step - loss: 0.0478 - acc: 0.9842 - val_loss: 0.1781 - val_acc: 0.9426\nEpoch 3/80\n63000/63000 [==============================] - 30s 471us/step - loss: 0.0353 - acc: 0.9891 - val_loss: 0.0701 - val_acc: 0.9766\nEpoch 4/80\n63000/63000 [==============================] - 30s 471us/step - loss: 0.0292 - acc: 0.9907 - val_loss: 0.0387 - val_acc: 0.9890\nEpoch 5/80\n63000/63000 [==============================] - 30s 473us/step - loss: 0.0237 - acc: 0.9925 - val_loss: 0.1911 - val_acc: 0.9254\nEpoch 6/80\n63000/63000 [==============================] - 30s 470us/step - loss: 0.0195 - acc: 0.9940 - val_loss: 0.3157 - val_acc: 0.8936\nEpoch 7/80\n63000/63000 [==============================] - 30s 469us/step - loss: 0.0183 - acc: 0.9945 - val_loss: 0.0464 - val_acc: 0.9847\nEpoch 8/80\n63000/63000 [==============================] - 30s 472us/step - loss: 0.0144 - acc: 0.9955 - val_loss: 0.0153 - val_acc: 0.9950\nEpoch 9/80\n63000/63000 [==============================] - 30s 468us/step - loss: 0.0122 - acc: 0.9962 - val_loss: 0.0357 - val_acc: 0.9931\nEpoch 10/80\n63000/63000 [==============================] - 30s 469us/step - loss: 0.0118 - acc: 0.9966 - val_loss: 0.0175 - val_acc: 0.9957\nEpoch 11/80\n63000/63000 [==============================] - 30s 472us/step - loss: 0.0114 - acc: 0.9969 - val_loss: 0.0346 - val_acc: 0.9924\nEpoch 12/80\n63000/63000 [==============================] - 30s 473us/step - loss: 0.0095 - acc: 0.9973 - val_loss: 0.0335 - val_acc: 0.9923\nEpoch 13/80\n63000/63000 [==============================] - 30s 470us/step - loss: 0.0089 - acc: 0.9975 - val_loss: 0.0187 - val_acc: 0.9947\nEpoch 14/80\n63000/63000 [==============================] - 30s 469us/step - loss: 0.0088 - acc: 0.9976 - val_loss: 0.0195 - val_acc: 0.9954\nEpoch 15/80\n63000/63000 [==============================] - 30s 469us/step - loss: 0.0078 - acc: 0.9979 - val_loss: 0.0191 - val_acc: 0.9963\nEpoch 16/80\n63000/63000 [==============================] - 30s 472us/step - loss: 0.0070 - acc: 0.9983 - val_loss: 0.0293 - val_acc: 0.9943\nEpoch 17/80\n63000/63000 [==============================] - 30s 468us/step - loss: 0.0077 - acc: 0.9980 - val_loss: 0.3062 - val_acc: 0.9326\nEpoch 18/80\n63000/63000 [==============================] - 30s 469us/step - loss: 0.0054 - acc: 0.9985 - val_loss: 0.1900 - val_acc: 0.9586\n\nEpoch 00018: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\nEpoch 19/80\n63000/63000 [==============================] - 30s 472us/step - loss: 0.0016 - acc: 0.9996 - val_loss: 0.0134 - val_acc: 0.9977\nEpoch 20/80\n63000/63000 [==============================] - 30s 469us/step - loss: 5.7911e-04 - acc: 0.9999 - val_loss: 0.0169 - val_acc: 0.9974\nEpoch 21/80\n63000/63000 [==============================] - 30s 472us/step - loss: 3.5007e-04 - acc: 0.9999 - val_loss: 0.0155 - val_acc: 0.9981\nEpoch 22/80\n63000/63000 [==============================] - 30s 476us/step - loss: 5.3868e-04 - acc: 0.9998 - val_loss: 0.0155 - val_acc: 0.9979\nEpoch 23/80\n63000/63000 [==============================] - 30s 469us/step - loss: 3.6740e-04 - acc: 0.9999 - val_loss: 0.0179 - val_acc: 0.9977\nEpoch 24/80\n32320/63000 [==============>...............] - ETA: 13s - loss: 1.1531e-04 - acc: 1.0000\n```\n\n# Results\n\nIn [12]:\n\n```py\nclf=model\ny_pred_proba = clf.predict_proba(X_tr_2)\n\ny_pred = clf.predict_classes(X_tr_2)\nconf_mat = confusion_matrix(Y_tr_2, y_pred)\nfig, ax = plt.subplots(figsize=(10,10))\n\nsns.heatmap(conf_mat, annot=True, fmt='d', xticklabels=['0','1'], yticklabels=['0','1'])\nplt.ylabel('Actual')\nplt.xlabel('Predicted')\nplt.show()\n\nprint(classification_report(Y_tr_2, y_pred, target_names=['0','1']))\nprint(\"\\n\\n AUC: {:<0.4f}\".format(roc_auc_score(Y_tr_2,y_pred_proba)))\n\n```\n\n![](data-augmentation-vgg16-cnn_files/__results___20_0.png)\n\n```\n              precision    recall  f1-score   support\n\n           0       1.00      1.00      1.00     17456\n           1       1.00      1.00      1.00     52544\n\n   micro avg       1.00      1.00      1.00     70000\n   macro avg       1.00      1.00      1.00     70000\nweighted avg       1.00      1.00      1.00     70000\n\n AUC: 1.0000\n\n```\n\n# Submit\n\nIn [13]:\n\n```py\ntest_df = pd.read_csv('../input/aerial-cactus-identification/sample_submission.csv')\nX_test = []\nimges = test_df['id'].values\nfor img_id in tqdm_notebook(imges):\n    X_test.append(cv2.imread(test_dir + img_id))     \nX_test = np.asarray(X_test)\nX_test = X_test.astype('float32')\nX_test /= 255\n\ny_test_pred  = model.predict_proba(X_test)\n\ntest_df['has_cactus'] = y_test_pred\ntest_df.to_csv('tf_learning_vgg16_aug2_80epoch.csv', index=False)\n\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/detecting-cactus-with-kekas.md",
    "content": "# Detecting cactus with kekas\n\n> Author: https://www.kaggle.com/artgor\n\n> From: https://www.kaggle.com/artgor/detecting-cactus-with-kekas\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\n## General information\n\n![](detecting-cactus-with-kekas_files/cactus_0163.jpg)\n\nResearchers in Mexico have created the VIGIA project, aiming to build a system for autonomous surveillance of protected areas. One of the first steps is being able to recognize the vegetation in the area. In this competition we are trying to identify whether there is a cactus in the image.\n\nIn this kernel I use kekas ([https://github.com/belskikh/kekas](https://github.com/belskikh/kekas)) as a wrapper for Pytorch.\n\nMost of the code is taken from my other kernel: [https://www.kaggle.com/artgor/cancer-detection-with-kekas](https://www.kaggle.com/artgor/cancer-detection-with-kekas)\n\nCodeIn [1]:\n\n```py\n# libraries\nimport numpy as np\nimport pandas as pd\nimport os\nimport cv2\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\nimport torch\nfrom torch.utils.data import TensorDataset, DataLoader,Dataset\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\nimport torchvision.transforms as transforms\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nimport time \nfrom PIL import Image\ntrain_on_gpu = True\nfrom torch.utils.data.sampler import SubsetRandomSampler\nfrom torch.optim.lr_scheduler import StepLR, ReduceLROnPlateau, CosineAnnealingLR\nfrom sklearn.metrics import accuracy_score\nimport cv2\n\n```\n\nSome of good libraries for DL aren't available in Docker with GPU by default, so it is necessary to install them. (don't forget to turn on internet connection in kernels).\n\nIn [2]:\n\n```py\n!pip install albumentations > /dev/null 2>&1\n!pip install pretrainedmodels > /dev/null 2>&1\n!pip install kekas > /dev/null 2>&1\n!pip install adabound > /dev/null 2>&1\n\n```\n\nCodeIn [3]:\n\n```py\n# more imports\nimport albumentations\nfrom albumentations import torch as AT\nimport pretrainedmodels\nimport adabound\n\nfrom kekas import Keker, DataOwner, DataKek\nfrom kekas.transformations import Transformer, to_torch, normalize\nfrom kekas.metrics import accuracy\nfrom kekas.modules import Flatten, AdaptiveConcatPool2d\nfrom kekas.callbacks import Callback, Callbacks, DebuggerCallback\nfrom kekas.utils import DotDict\n\n```\n\n```\n/opt/conda/lib/python3.6/site-packages/kekas/keker.py:9: UserWarning: Error 'No module named 'apex''' during importing apex library. To use mixed precison you should install it from https://github.com/NVIDIA/apex\n  warnings.warn(f\"Error '{e}'' during importing apex library. To use mixed precison\"\n\n```\n\n## Data overview\n\nIn [4]:\n\n```py\nlabels = pd.read_csv('../input/train.csv')\nfig = plt.figure(figsize=(25, 8))\ntrain_imgs = os.listdir(\"../input/train/train\")\nfor idx, img in enumerate(np.random.choice(train_imgs, 20)):\n    ax = fig.add_subplot(4, 20//4, idx+1, xticks=[], yticks=[])\n    im = Image.open(\"../input/train/train/\" + img)\n    plt.imshow(im)\n    lab = labels.loc[labels['id'] == img, 'has_cactus'].values[0]\n    ax.set_title(f'Label: {lab}')\n\n```\n\n![](detecting-cactus-with-kekas_files/__results___6_0.png)\n\nImages were resized, so I can see almost nothing in them...\n\nKekas accepts pandas DataFrame as an input and iterates over it to get image names and labels\n\nIn [5]:\n\n```py\ntest_img = os.listdir('../input/test/test')\ntest_df = pd.DataFrame(test_img, columns=['id'])\ntest_df['has_cactus'] = -1\ntest_df['data_type'] = 'test'\n\nlabels['has_cactus'] = labels['has_cactus'].astype(int)\nlabels['data_type'] = 'train'\n\nlabels.head()\n\n```\n\nOut[5]:\n\n|  | id | has_cactus | data_type |\n| --- | --- | --- | --- |\n| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 | train |\n| --- | --- | --- | --- |\n| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 | train |\n| --- | --- | --- | --- |\n| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 | train |\n| --- | --- | --- | --- |\n| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 | train |\n| --- | --- | --- | --- |\n| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 | train |\n| --- | --- | --- | --- |\n\nIn [6]:\n\n```py\nlabels.loc[labels['data_type'] == 'train', 'has_cactus'].value_counts()\n\n```\n\nOut[6]:\n\n```\n1    13136\n0     4364\nName: has_cactus, dtype: int64\n```\n\nWe have some disbalance in the data, but it isn't too big.\n\nIn [7]:\n\n```py\n# splitting data into train and validation\ntrain, valid = train_test_split(labels, stratify=labels.has_cactus, test_size=0.2)\n\n```\n\n### Reader function\n\nAt first it is necessary to create a reader function, which will open images. It accepts i and row as input (like from pandas iterrows). The function should return a dictionary with image and label. [:,:,::-1] - is a neat trick which converts BGR images to RGB, it works faster that converting to RGB by usual means.\n\nIn [8]:\n\n```py\ndef reader_fn(i, row):\n    image = cv2.imread(f\"../input/{row['data_type']}/{row['data_type']}/{row['id']}\")[:,:,::-1] # BGR -> RGB\n    label = torch.Tensor([row[\"has_cactus\"]])\n    return {\"image\": image, \"label\": label}\n\n```\n\n### Data transformation\n\nNext step is defining data transformations and augmentations. This differs from standard PyTorch way. We define resizing, augmentations and normalizing separately, this allows to easily create separate transformers for train and valid/test data.\n\nAt first we define augmentations. We create a function with a list of augmentations (I prefer albumentation library: [https://github.com/albu/albumentations](https://github.com/albu/albumentations))\n\nIn [9]:\n\n```py\ndef augs(p=0.5):\n    return albumentations.Compose([\n        albumentations.HorizontalFlip(),\n        albumentations.VerticalFlip(),\n        albumentations.RandomBrightness(),\n    ], p=p)\n\n```\n\nNow we create a transforming function. It heavily uses Transformer from kekas.\n\n*   The first step is defining resizing. You can change arguments of function if you want images to have different height and width, otherwis you can leave it as it is.\n*   Next step is defining augmentations. Here we provide the key of image which is defined in reader_fn;\n*   The third step is defining final transformation to tensor and normalizing;\n*   After this we can compose separate transformations for train and valid/test data;\n\nIn [10]:\n\n```py\ndef get_transforms(dataset_key, size, p):\n\n    PRE_TFMS = Transformer(dataset_key, lambda x: cv2.resize(x, (size, size)))\n\n    AUGS = Transformer(dataset_key, lambda x: augs()(image=x)[\"image\"])\n\n    NRM_TFMS = transforms.Compose([\n        Transformer(dataset_key, to_torch()),\n        Transformer(dataset_key, normalize())\n    ])\n\n    train_tfms = transforms.Compose([PRE_TFMS, AUGS, NRM_TFMS])\n    val_tfms = transforms.Compose([PRE_TFMS, NRM_TFMS])\n\n    return train_tfms, val_tfms\n\n```\n\nIn [11]:\n\n```py\ntrain_tfms, val_tfms = get_transforms(\"image\", 32, 0.5)\n\n```\n\nNow we can create a DataKek, which is similar to creating dataset in Pytorch. We define the data, reader function and transformation.Then we can define standard PyTorch DataLoader.\n\nIn [12]:\n\n```py\ntrain_dk = DataKek(df=train, reader_fn=reader_fn, transforms=train_tfms)\nval_dk = DataKek(df=valid, reader_fn=reader_fn, transforms=val_tfms)\n\nbatch_size = 64\nworkers = 0\n\ntrain_dl = DataLoader(train_dk, batch_size=batch_size, num_workers=workers, shuffle=True, drop_last=True)\nval_dl = DataLoader(val_dk, batch_size=batch_size, num_workers=workers, shuffle=False)\n\n```\n\nIn [13]:\n\n```py\ntest_dk = DataKek(df=test_df, reader_fn=reader_fn, transforms=val_tfms)\ntest_dl = DataLoader(test_dk, batch_size=batch_size, num_workers=workers, shuffle=False)\n\n```\n\n### Building a neural net\n\nHere we define the architecture of the neural net.\n\n*   Pre-trained backbone is taken from pretrainedmodels: [https://github.com/Cadene/pretrained-models.pytorch](https://github.com/Cadene/pretrained-models.pytorch) Here I take densenet169\n*   We also define changes to the architecture. For example, we take off the last layer and add a custom head with nn.Sequential. AdaptiveConcatPool2d is a layer in kekas, which concats AdaptiveMaxPooling and AdaptiveAveragePooling\n\nIn [14]:\n\n```py\nclass Net(nn.Module):\n    def __init__(\n            self,\n            num_classes: int,\n            p: float = 0.2,\n            pooling_size: int = 2,\n            last_conv_size: int = 1664,\n            arch: str = \"densenet169\",\n            pretrained: str = \"imagenet\") -> None:\n        \"\"\"A simple model to finetune.\n\n Args:\n num_classes: the number of target classes, the size of the last layer's output\n p: dropout probability\n pooling_size: the size of the result feature map after adaptive pooling layer\n last_conv_size: size of the flatten last backbone conv layer\n arch: the name of the architecture form pretrainedmodels\n pretrained: the mode for pretrained model from pretrainedmodels\n \"\"\"\n        super().__init__()\n        net = pretrainedmodels.__dict__[arch](pretrained=pretrained)\n        modules = list(net.children())[:-1]  # delete last layer\n        # add custom head\n        modules += [nn.Sequential(\n            # AdaptiveConcatPool2d is a concat of AdaptiveMaxPooling and AdaptiveAveragePooling \n            # AdaptiveConcatPool2d(size=pooling_size),\n            Flatten(),\n            nn.BatchNorm1d(1664),\n            nn.Dropout(p),\n            nn.Linear(1664, num_classes)\n        )]\n        self.net = nn.Sequential(*modules)\n\n    def forward(self, x):\n        logits = self.net(x)\n        return logits\n\n```\n\nThe data for training needs to be transformed one more time - we define DataOwner, which contains all the data. For now let's define it for train and valid. Next we define model and loss. As I choose BCEWithLogitsLoss, we can set the number of classes for output to 1.\n\nIn [15]:\n\n```py\ndataowner = DataOwner(train_dl, val_dl, None)\nmodel = Net(num_classes=1)\ncriterion = nn.BCEWithLogitsLoss()\n\n```\n\n```\nDownloading: \"http://data.lip6.fr/cadene/pretrainedmodels/densenet169-f470b90a4.pth\" to /tmp/.torch/models/densenet169-f470b90a4.pth\n57372314it [00:03, 14671003.61it/s]\n\n```\n\nAnd now we define what will the model do with the data. For example we could slice the output and take only a part of it. For now we will simply return the output of the model.\n\nIn [16]:\n\n```py\ndef step_fn(model: torch.nn.Module,\n            batch: torch.Tensor) -> torch.Tensor:\n    \"\"\"Determine what your model will do with your data.\n\n Args:\n model: the pytorch module to pass input in\n batch: the batch of data from the DataLoader\n\n Returns:\n The models forward pass results\n \"\"\"\n\n    inp = batch[\"image\"]\n    return model(inp)\n\n```\n\nDefining custom metrics\n\nIn [17]:\n\n```py\ndef bce_accuracy(target: torch.Tensor,\n                 preds: torch.Tensor,\n                 thresh: bool = 0.5) -> float:\n    target = target.cpu().detach().numpy()\n    preds = (torch.sigmoid(preds).cpu().detach().numpy() > thresh).astype(int)\n    return accuracy_score(target, preds)\n\ndef roc_auc(target: torch.Tensor,\n                 preds: torch.Tensor) -> float:\n    target = target.cpu().detach().numpy()\n    preds = torch.sigmoid(preds).cpu().detach().numpy()\n    return roc_auc_score(target, preds)\n\n```\n\n### Keker\n\nNow we can define the Keker - the core Kekas class for training the model.\n\nHere we define everything which is necessary for training:\n\n*   the model which was defined earlier;\n*   dataowner containing the data for training and validation;\n*   criterion;\n*   step function;\n*   the key of labels, which was defined in the reader function;\n*   the dictionary with metrics (there can be several of them);\n*   The optimizer and its parameters;\n\nIn [18]:\n\n```py\nkeker = Keker(model=model,\n              dataowner=dataowner,\n              criterion=criterion,\n              step_fn=step_fn,\n              target_key=\"label\",\n              metrics={\"acc\": bce_accuracy, 'auc': roc_auc},\n              opt=torch.optim.SGD,\n              opt_params={\"momentum\": 0.99})\n\n```\n\nIn [19]:\n\n```py\nkeker.unfreeze(model_attr=\"net\")\n\nlayer_num = -1\nkeker.freeze_to(layer_num, model_attr=\"net\")\n\n```\n\nIn [20]:\n\n```py\nkeker.kek_one_cycle(max_lr=1e-2,                  # the maximum learning rate\n                    cycle_len=5,                  # number of epochs, actually, but not exactly\n                    momentum_range=(0.95, 0.85),  # range of momentum changes\n                    div_factor=25,                # max_lr / min_lr\n                    increase_fraction=0.3,        # the part of cycle when learning rate increases\n                    logdir='train_logs')\nkeker.plot_kek('train_logs')\n\n```\n\n```\nEpoch 1/5: 100% 218/218 [00:52<00:00,  4.94it/s, loss=0.0502, val_loss=0.0337, acc=0.9896, auc=0.9995]\nEpoch 2/5: 100% 218/218 [00:42<00:00,  6.22it/s, loss=0.0131, val_loss=0.0152, acc=0.9955, auc=0.9999]\nEpoch 3/5: 100% 218/218 [00:41<00:00,  6.37it/s, loss=0.0146, val_loss=0.0112, acc=0.9955, auc=1.0000]\nEpoch 4/5: 100% 218/218 [00:43<00:00,  5.73it/s, loss=0.0097, val_loss=0.0109, acc=0.9955, auc=1.0000]\nEpoch 5/5: 100% 218/218 [00:41<00:00,  6.24it/s, loss=0.0107, val_loss=0.0092, acc=0.9957, auc=1.0000]\n\n```\n\n<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\" style=\"background: rgb(255, 255, 255) none repeat scroll 0% 0%;\"><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; 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font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" x=\"40\" y=\"4.680000000000001\" data-unformatted=\"val/batch/loss\" data-math=\"N\">val/batch/loss</text></g></g></g></g><g class=\"g-gtitle\"><text class=\"gtitle\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 17px; fill: rgb(68, 68, 68); opacity: 1; font-weight: normal; white-space: pre;\" x=\"346.7\" y=\"50\" text-anchor=\"middle\" dy=\"0em\" data-unformatted=\"batch/loss\" data-math=\"N\">batch/loss</text></g></g></svg>[<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 132 132\" height=\"1em\" width=\"1em\"><title>plotly-logomark</title></svg>](https://plot.ly/)<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\" style=\"background: rgb(255, 255, 255) none repeat scroll 0% 0%;\"><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0\" data-math=\"N\" transform=\"translate(80,0)\">0</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"500\" data-math=\"N\" transform=\"translate(246.42,0)\">500</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"1000\" data-math=\"N\" transform=\"translate(412.84,0)\">1000</text></g></g><g class=\"yaxislayer-above\"><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.6\" data-math=\"N\" transform=\"translate(0,382.21)\">0.6</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.7\" data-math=\"N\" transform=\"translate(0,315.97)\">0.7</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.8\" data-math=\"N\" transform=\"translate(0,249.73)\">0.8</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.9\" data-math=\"N\" transform=\"translate(0,183.49)\">0.9</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"1\" data-math=\"N\" transform=\"translate(0,117.25)\">1</text></g></g></g></g></svg><svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\"><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(543.0799999999999, 100)\"><g class=\"scrollbox\" transform=\"translate(0, 0)\" clip-path=\"url(#legende39934)\"><g class=\"groups\"><g class=\"traces\" style=\"opacity: 1;\" transform=\"translate(0, 14.5)\"><text class=\"legendtext user-select-none\" text-anchor=\"start\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" x=\"40\" y=\"4.680000000000001\" data-unformatted=\"train/batch/acc\" data-math=\"N\">train/batch/acc</text></g><g class=\"traces\" style=\"opacity: 1;\" transform=\"translate(0, 33.5)\"><text class=\"legendtext user-select-none\" text-anchor=\"start\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" x=\"40\" y=\"4.680000000000001\" data-unformatted=\"val/batch/acc\" data-math=\"N\">val/batch/acc</text></g></g></g></g><g class=\"g-gtitle\"><text class=\"gtitle\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 17px; fill: rgb(68, 68, 68); opacity: 1; font-weight: normal; white-space: pre;\" x=\"346.7\" y=\"50\" text-anchor=\"middle\" dy=\"0em\" data-unformatted=\"batch/acc\" data-math=\"N\">batch/acc</text></g></g></svg>[<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 132 132\" height=\"1em\" width=\"1em\"><title>plotly-logomark</title></svg>](https://plot.ly/)<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\" style=\"background: rgb(255, 255, 255) none repeat scroll 0% 0%;\"><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0\" data-math=\"N\" transform=\"translate(80,0)\">0</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"500\" data-math=\"N\" transform=\"translate(246.06,0)\">500</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"1000\" data-math=\"N\" transform=\"translate(412.11,0)\">1000</text></g></g><g class=\"yaxislayer-above\"><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.5\" data-math=\"N\" transform=\"translate(0,433.09)\">0.5</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.6\" data-math=\"N\" transform=\"translate(0,369.92)\">0.6</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.7\" data-math=\"N\" transform=\"translate(0,306.75)\">0.7</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.8\" data-math=\"N\" transform=\"translate(0,243.59)\">0.8</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.9\" data-math=\"N\" transform=\"translate(0,180.42000000000002)\">0.9</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"1\" data-math=\"N\" transform=\"translate(0,117.25)\">1</text></g></g></g></g></svg><svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\"><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(542.06, 100)\"><g class=\"scrollbox\" transform=\"translate(0, 0)\" clip-path=\"url(#legend3f014a)\"><g class=\"groups\"><g class=\"traces\" style=\"opacity: 1;\" transform=\"translate(0, 14.5)\"><text class=\"legendtext user-select-none\" text-anchor=\"start\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" x=\"40\" y=\"4.680000000000001\" data-unformatted=\"train/batch/auc\" data-math=\"N\">train/batch/auc</text></g><g class=\"traces\" style=\"opacity: 1;\" transform=\"translate(0, 33.5)\"><text class=\"legendtext user-select-none\" text-anchor=\"start\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" x=\"40\" y=\"4.680000000000001\" data-unformatted=\"val/batch/auc\" data-math=\"N\">val/batch/auc</text></g></g></g></g><g class=\"g-gtitle\"><text class=\"gtitle\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 17px; fill: rgb(68, 68, 68); opacity: 1; font-weight: normal; white-space: pre;\" x=\"346.7\" y=\"50\" text-anchor=\"middle\" dy=\"0em\" data-unformatted=\"batch/auc\" data-math=\"N\">batch/auc</text></g></g></svg>[<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 132 132\" height=\"1em\" width=\"1em\"><title>plotly-logomark</title></svg>](https://plot.ly/)<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\" style=\"background: rgb(255, 255, 255) none repeat scroll 0% 0%;\"><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0\" data-math=\"N\" transform=\"translate(80,0)\">0</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"200\" data-math=\"N\" transform=\"translate(177.89,0)\">200</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"400\" data-math=\"N\" transform=\"translate(275.78,0)\">400</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"600\" data-math=\"N\" transform=\"translate(373.66,0)\">600</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"800\" data-math=\"N\" transform=\"translate(471.55,0)\">800</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"1000\" data-math=\"N\" transform=\"translate(569.44,0)\">1000</text></g></g><g class=\"yaxislayer-above\"><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0\" data-math=\"N\" transform=\"translate(0,440.69)\">0</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.002\" data-math=\"N\" transform=\"translate(0,376)\">0.002</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.004\" data-math=\"N\" transform=\"translate(0,311.31)\">0.004</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.006\" data-math=\"N\" transform=\"translate(0,246.62)\">0.006</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.008\" data-math=\"N\" transform=\"translate(0,181.94)\">0.008</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.01\" data-math=\"N\" transform=\"translate(0,117.25)\">0.01</text></g></g></g></g></svg><svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\"><g class=\"infolayer\"><g class=\"g-gtitle\"><text class=\"gtitle\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 17px; fill: rgb(68, 68, 68); opacity: 1; font-weight: normal; white-space: pre;\" x=\"346.7\" y=\"50\" text-anchor=\"middle\" dy=\"0em\" data-unformatted=\"batch/lr\" data-math=\"N\">batch/lr</text></g></g></svg>[<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 132 132\" height=\"1em\" width=\"1em\"><title>plotly-logomark</title></svg>](https://plot.ly/)In [21]:\n\n```py\nkeker.kek_one_cycle(max_lr=1e-3,                  # the maximum learning rate\n                    cycle_len=5,                  # number of epochs, actually, but not exactly\n                    momentum_range=(0.95, 0.85),  # range of momentum changes\n                    div_factor=25,                # max_lr / min_lr\n                    increase_fraction=0.2,        # the part of cycle when learning rate increases\n                    logdir='train_logs1')\nkeker.plot_kek('train_logs1')\n\n```\n\n```\nEpoch 1/5: 100% 218/218 [00:41<00:00,  6.29it/s, loss=0.0149, val_loss=0.0089, acc=0.9963, auc=1.0000]\nEpoch 2/5: 100% 218/218 [00:41<00:00,  6.34it/s, loss=0.0221, val_loss=0.0098, acc=0.9966, auc=1.0000]\nEpoch 3/5: 100% 218/218 [00:42<00:00,  6.39it/s, loss=0.0122, val_loss=0.0094, acc=0.9960, auc=1.0000]\nEpoch 4/5: 100% 218/218 [00:41<00:00,  5.93it/s, loss=0.0119, val_loss=0.0087, acc=0.9963, auc=1.0000]\nEpoch 5/5: 100% 218/218 [00:41<00:00,  6.48it/s, loss=0.0106, val_loss=0.0086, acc=0.9974, auc=1.0000]\n\n```\n\n<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\" style=\"background: rgb(255, 255, 255) none repeat scroll 0% 0%;\"><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0\" data-math=\"N\" transform=\"translate(80,0)\">0</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"500\" data-math=\"N\" transform=\"translate(245.32,0)\">500</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"1000\" data-math=\"N\" transform=\"translate(410.65,0)\">1000</text></g></g><g class=\"yaxislayer-above\"><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0\" data-math=\"N\" transform=\"translate(0,428.18)\">0</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.02\" data-math=\"N\" transform=\"translate(0,387.22)\">0.02</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.04\" data-math=\"N\" transform=\"translate(0,346.27)\">0.04</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.06\" data-math=\"N\" transform=\"translate(0,305.31)\">0.06</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.08\" data-math=\"N\" transform=\"translate(0,264.35)\">0.08</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.1\" data-math=\"N\" transform=\"translate(0,223.39)\">0.1</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.12\" data-math=\"N\" transform=\"translate(0,182.44)\">0.12</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.14\" data-math=\"N\" transform=\"translate(0,141.48)\">0.14</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.16\" data-math=\"N\" transform=\"translate(0,100.52)\">0.16</text></g></g></g></g></svg><svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\"><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(540.02, 100)\"><g class=\"scrollbox\" transform=\"translate(0, 0)\" clip-path=\"url(#legend7718e1)\"><g class=\"groups\"><g class=\"traces\" style=\"opacity: 1;\" transform=\"translate(0, 14.5)\"><text class=\"legendtext user-select-none\" text-anchor=\"start\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" x=\"40\" y=\"4.680000000000001\" data-unformatted=\"train/batch/loss\" data-math=\"N\">train/batch/loss</text></g><g class=\"traces\" style=\"opacity: 1;\" transform=\"translate(0, 33.5)\"><text class=\"legendtext user-select-none\" text-anchor=\"start\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" x=\"40\" y=\"4.680000000000001\" data-unformatted=\"val/batch/loss\" data-math=\"N\">val/batch/loss</text></g></g></g></g><g class=\"g-gtitle\"><text class=\"gtitle\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 17px; fill: rgb(68, 68, 68); opacity: 1; font-weight: normal; white-space: pre;\" x=\"346.7\" y=\"50\" text-anchor=\"middle\" dy=\"0em\" data-unformatted=\"batch/loss\" data-math=\"N\">batch/loss</text></g></g></svg>[<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 132 132\" height=\"1em\" width=\"1em\"><title>plotly-logomark</title></svg>](https://plot.ly/)<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\" style=\"background: rgb(255, 255, 255) none repeat scroll 0% 0%;\"><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0\" data-math=\"N\" transform=\"translate(80,0)\">0</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"500\" data-math=\"N\" transform=\"translate(246.42,0)\">500</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"1000\" data-math=\"N\" transform=\"translate(412.84,0)\">1000</text></g></g><g class=\"yaxislayer-above\"><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.96\" data-math=\"N\" transform=\"translate(0,382.21)\">0.96</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.97\" data-math=\"N\" transform=\"translate(0,315.97)\">0.97</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.98\" data-math=\"N\" transform=\"translate(0,249.73)\">0.98</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.99\" data-math=\"N\" transform=\"translate(0,183.49)\">0.99</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"1\" data-math=\"N\" transform=\"translate(0,117.25)\">1</text></g></g></g></g></svg><svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\"><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(543.0799999999999, 100)\"><g class=\"scrollbox\" transform=\"translate(0, 0)\" clip-path=\"url(#legend46d60f)\"><g class=\"groups\"><g class=\"traces\" style=\"opacity: 1;\" transform=\"translate(0, 14.5)\"><text class=\"legendtext user-select-none\" text-anchor=\"start\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" x=\"40\" y=\"4.680000000000001\" data-unformatted=\"train/batch/acc\" data-math=\"N\">train/batch/acc</text></g><g class=\"traces\" style=\"opacity: 1;\" transform=\"translate(0, 33.5)\"><text class=\"legendtext user-select-none\" text-anchor=\"start\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" x=\"40\" y=\"4.680000000000001\" data-unformatted=\"val/batch/acc\" data-math=\"N\">val/batch/acc</text></g></g></g></g><g class=\"g-gtitle\"><text class=\"gtitle\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 17px; fill: rgb(68, 68, 68); opacity: 1; font-weight: normal; white-space: pre;\" x=\"346.7\" y=\"50\" text-anchor=\"middle\" dy=\"0em\" data-unformatted=\"batch/acc\" data-math=\"N\">batch/acc</text></g></g></svg>[<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 132 132\" height=\"1em\" width=\"1em\"><title>plotly-logomark</title></svg>](https://plot.ly/)<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\" style=\"background: rgb(255, 255, 255) none repeat scroll 0% 0%;\"><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0\" data-math=\"N\" transform=\"translate(80,0)\">0</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"500\" data-math=\"N\" transform=\"translate(246.06,0)\">500</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"1000\" data-math=\"N\" transform=\"translate(412.11,0)\">1000</text></g></g><g class=\"yaxislayer-above\"><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.9975\" data-math=\"N\" transform=\"translate(0,427.36)\">0.9975</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.998\" data-math=\"N\" transform=\"translate(0,365.34)\">0.998</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.9985\" data-math=\"N\" transform=\"translate(0,303.32)\">0.9985</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.999\" data-math=\"N\" transform=\"translate(0,241.29)\">0.999</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.9995\" data-math=\"N\" transform=\"translate(0,179.26999999999998)\">0.9995</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"1\" data-math=\"N\" transform=\"translate(0,117.25)\">1</text></g></g></g></g></svg><svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\"><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(542.06, 100)\"><g class=\"scrollbox\" transform=\"translate(0, 0)\" clip-path=\"url(#legend8b7908)\"><g class=\"groups\"><g class=\"traces\" style=\"opacity: 1;\" transform=\"translate(0, 14.5)\"><text class=\"legendtext user-select-none\" text-anchor=\"start\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" x=\"40\" y=\"4.680000000000001\" data-unformatted=\"train/batch/auc\" data-math=\"N\">train/batch/auc</text></g><g class=\"traces\" style=\"opacity: 1;\" transform=\"translate(0, 33.5)\"><text class=\"legendtext user-select-none\" text-anchor=\"start\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" x=\"40\" y=\"4.680000000000001\" data-unformatted=\"val/batch/auc\" data-math=\"N\">val/batch/auc</text></g></g></g></g><g class=\"g-gtitle\"><text class=\"gtitle\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 17px; fill: rgb(68, 68, 68); opacity: 1; font-weight: normal; white-space: pre;\" x=\"346.7\" y=\"50\" text-anchor=\"middle\" dy=\"0em\" data-unformatted=\"batch/auc\" data-math=\"N\">batch/auc</text></g></g></svg>[<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 132 132\" height=\"1em\" width=\"1em\"><title>plotly-logomark</title></svg>](https://plot.ly/)<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\" style=\"background: rgb(255, 255, 255) none repeat scroll 0% 0%;\"><g class=\"cartesianlayer\"><g class=\"subplot xy\"><g class=\"xaxislayer-above\"><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0\" data-math=\"N\" transform=\"translate(80,0)\">0</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"200\" data-math=\"N\" transform=\"translate(177.89,0)\">200</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"400\" data-math=\"N\" transform=\"translate(275.78,0)\">400</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"600\" data-math=\"N\" transform=\"translate(373.66,0)\">600</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"800\" data-math=\"N\" transform=\"translate(471.55,0)\">800</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"458\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"1000\" data-math=\"N\" transform=\"translate(569.44,0)\">1000</text></g></g><g class=\"yaxislayer-above\"><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0\" data-math=\"N\" transform=\"translate(0,440.69)\">0</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.0002\" data-math=\"N\" transform=\"translate(0,376)\">0.0002</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.0004\" data-math=\"N\" transform=\"translate(0,311.31)\">0.0004</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.0006\" data-math=\"N\" transform=\"translate(0,246.63)\">0.0006</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.0008\" data-math=\"N\" transform=\"translate(0,181.94)\">0.0008</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 12px; fill: rgb(68, 68, 68); fill-opacity: 1; white-space: pre;\" data-unformatted=\"0.001\" data-math=\"N\" transform=\"translate(0,117.25)\">0.001</text></g></g></g></g></svg><svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xlink=\"http://www.w3.org/1999/xlink\" width=\"693.4\" height=\"525\"><g class=\"infolayer\"><g class=\"g-gtitle\"><text class=\"gtitle\" style=\"font-family: &quot;Open Sans&quot;, verdana, arial, sans-serif; font-size: 17px; fill: rgb(68, 68, 68); opacity: 1; font-weight: normal; white-space: pre;\" x=\"346.7\" y=\"50\" text-anchor=\"middle\" dy=\"0em\" data-unformatted=\"batch/lr\" data-math=\"N\">batch/lr</text></g></g></svg>[<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 132 132\" height=\"1em\" width=\"1em\"><title>plotly-logomark</title></svg>](https://plot.ly/)\n\n### Predicting and TTA\n\nSimply predicting on test data is okay, but it is better to use TTA - test time augmentation. Let's see how it can be done with Kekas.\n\n*   define augmentations;\n*   define augmentation function;\n*   create objects with these augmentations;\n*   put these objects into a single dictionary;\n\nIn [22]:\n\n```py\npreds = keker.predict_loader(loader=test_dl)\n\n```\n\n```\nPredict: 100% 63/63 [00:11<00:00,  6.02it/s]\n\n```\n\nIn [23]:\n\n```py\n# flip_ = albumentations.HorizontalFlip(always_apply=True)\n# transpose_ = albumentations.Transpose(always_apply=True)\n\n# def insert_aug(aug, dataset_key=\"image\", size=224): \n#     PRE_TFMS = Transformer(dataset_key, lambda x: cv2.resize(x, (size, size)))\n\n#     AUGS = Transformer(dataset_key, lambda x: aug(image=x)[\"image\"])\n\n#     NRM_TFMS = transforms.Compose([\n#         Transformer(dataset_key, to_torch()),\n#         Transformer(dataset_key, normalize())\n#     ])\n\n#     tfm = transforms.Compose([PRE_TFMS, AUGS, NRM_TFMS])\n#     return tfm\n\n# flip = insert_aug(flip_)\n# transpose = insert_aug(transpose_)\n\n# tta_tfms = {\"flip\": flip, \"transpose\": transpose}\n\n# # third, run TTA\n# keker.TTA(loader=test_dl,                # loader to predict on \n#           tfms=tta_tfms,                # list or dict of always applying transforms\n#           savedir=\"tta_preds1\",  # savedir\n#           prefix=\"preds\")               # (optional) name prefix. default is 'preds'\n\n```\n\nIn [24]:\n\n```py\n# prediction = np.zeros((test_df.shape[0], 1))\n# for i in os.listdir('tta_preds1'):\n#     pr = np.load('tta_preds1/' + i)\n#     prediction += pr\n# prediction = prediction / len(os.listdir('tta_preds1'))\n\n```\n\nIn [25]:\n\n```py\ntest_preds = pd.DataFrame({'imgs': test_df.id.values, 'preds': preds.reshape(-1,)})\ntest_preds.columns = ['id', 'has_cactus']\ntest_preds.to_csv('sub.csv', index=False)\ntest_preds.head()\n\n```\n\nOut[25]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 79ac4cc3b082e0a1defe1be601806efd.jpg | 4.901506 |\n| --- | --- | --- |\n| 1 | e880364d6521c6f3a27748ec62b0e335.jpg | 9.305355 |\n| --- | --- | --- |\n| 2 | 74912492b6cdf28c4bfb9c8e1d35af3e.jpg | 8.171706 |\n| --- | --- | --- |\n| 3 | 078cfa961183b30693ea2f13f5ff6d17.jpg | 8.186723 |\n| --- | --- | --- |\n| 4 | 7fd729184ef182899ce3e7a174fb9bc0.jpg | 9.269026 |\n| --- | --- | --- |"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/fast-fastai-with-condensenet.md",
    "content": "# Fast fastai with condensenet\n\n> Author: https://www.kaggle.com/interneuron\n\n> From: https://www.kaggle.com/interneuron/fast-fastai-with-condensenet\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\n> Score: 1.0000\n\nforked from [https://www.kaggle.com/kenseitrg/simple-fastai-exercise](https://www.kaggle.com/kenseitrg/simple-fastai-exercise)\n\nIn [1]:\n\n```py\n!pip install pytorchcv\n\n```\n\n```\nCollecting pytorchcv\n  Downloading https://files.pythonhosted.org/packages/20/8c/c9a820af0a5d56c4f5803a3138319ce76907c2b6db61fd9edd9dec483bb9/pytorchcv-0.0.42-py2.py3-none-any.whl (280kB)\n    100% |████████████████████████████████| 286kB 10.6MB/s \nRequirement already satisfied: requests in /opt/conda/lib/python3.6/site-packages (from pytorchcv) (2.21.0)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.6/site-packages (from pytorchcv) (1.16.2)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.6/site-packages (from requests->pytorchcv) (2019.3.9)\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/lib/python3.6/site-packages (from requests->pytorchcv) (3.0.4)\nRequirement already satisfied: urllib3<1.25,>=1.21.1 in /opt/conda/lib/python3.6/site-packages (from requests->pytorchcv) (1.22)\nRequirement already satisfied: idna<2.9,>=2.5 in /opt/conda/lib/python3.6/site-packages (from requests->pytorchcv) (2.6)\nInstalling collected packages: pytorchcv\nSuccessfully installed pytorchcv-0.0.42\n\n```\n\nIn [2]:\n\n```py\n!pip install fastai==1.0.47\n\n```\n\n```\nCollecting fastai==1.0.47\n  Downloading https://files.pythonhosted.org/packages/4b/92/134c4ce85851f6c9156e3363c7d396716a17dc9915b4921b490f96a5a4f2/fastai-1.0.47-py3-none-any.whl (205kB)\n    100% |████████████████████████████████| 215kB 33.0MB/s \nRequirement already satisfied: torch>=1.0.0 in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (1.0.1.post2)\nRequirement already satisfied: packaging in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (17.1)\nRequirement already satisfied: dataclasses; python_version < \"3.7\" in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (0.6)\nRequirement already satisfied: requests in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (2.21.0)\nRequirement already satisfied: nvidia-ml-py3 in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (7.352.0)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (1.1.0)\nRequirement already satisfied: numpy>=1.15 in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (1.16.2)\nRequirement already satisfied: pyyaml in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (3.12)\nRequirement already satisfied: bottleneck in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (1.2.1)\nRequirement already satisfied: fastprogress>=0.1.19 in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (0.1.20)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (3.0.3)\nRequirement already satisfied: pandas in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (0.23.4)\nRequirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (4.6.0)\nRequirement already satisfied: Pillow in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (5.1.0)\nRequirement already satisfied: typing in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (3.6.4)\nRequirement already satisfied: spacy>=2.0.18 in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (2.0.18)\nRequirement already satisfied: numexpr in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (2.6.5)\nRequirement already satisfied: torchvision in /opt/conda/lib/python3.6/site-packages (from fastai==1.0.47) (0.2.2)\nRequirement 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matplotlib->fastai==1.0.47) (1.0.1)\nRequirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib->fastai==1.0.47) (2.6.0)\nRequirement already satisfied: pytz>=2011k in /opt/conda/lib/python3.6/site-packages (from pandas->fastai==1.0.47) (2018.4)\nRequirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /opt/conda/lib/python3.6/site-packages (from spacy>=2.0.18->fastai==1.0.47) (1.0.0)\nRequirement already satisfied: cymem<2.1.0,>=2.0.2 in /opt/conda/lib/python3.6/site-packages (from spacy>=2.0.18->fastai==1.0.47) (2.0.2)\nRequirement already satisfied: preshed<2.1.0,>=2.0.1 in /opt/conda/lib/python3.6/site-packages (from spacy>=2.0.18->fastai==1.0.47) (2.0.1)\nRequirement already satisfied: thinc<6.13.0,>=6.12.1 in /opt/conda/lib/python3.6/site-packages (from spacy>=2.0.18->fastai==1.0.47) (6.12.1)\nRequirement already satisfied: plac<1.0.0,>=0.9.6 in /opt/conda/lib/python3.6/site-packages (from spacy>=2.0.18->fastai==1.0.47) (0.9.6)\nRequirement already satisfied: ujson>=1.35 in /opt/conda/lib/python3.6/site-packages (from spacy>=2.0.18->fastai==1.0.47) (1.35)\nRequirement already satisfied: dill<0.3,>=0.2 in /opt/conda/lib/python3.6/site-packages (from spacy>=2.0.18->fastai==1.0.47) (0.2.9)\nRequirement already satisfied: regex==2018.01.10 in /opt/conda/lib/python3.6/site-packages (from spacy>=2.0.18->fastai==1.0.47) (2018.1.10)\nRequirement already satisfied: setuptools in /opt/conda/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib->fastai==1.0.47) (39.1.0)\nRequirement already satisfied: msgpack<0.6.0,>=0.5.6 in /opt/conda/lib/python3.6/site-packages (from thinc<6.13.0,>=6.12.1->spacy>=2.0.18->fastai==1.0.47) (0.5.6)\nRequirement already satisfied: msgpack-numpy<0.4.4 in /opt/conda/lib/python3.6/site-packages (from thinc<6.13.0,>=6.12.1->spacy>=2.0.18->fastai==1.0.47) (0.4.3.2)\nRequirement already satisfied: cytoolz<0.10,>=0.9.0 in /opt/conda/lib/python3.6/site-packages (from thinc<6.13.0,>=6.12.1->spacy>=2.0.18->fastai==1.0.47) (0.9.0.1)\nRequirement already satisfied: wrapt<1.11.0,>=1.10.0 in /opt/conda/lib/python3.6/site-packages (from thinc<6.13.0,>=6.12.1->spacy>=2.0.18->fastai==1.0.47) (1.10.11)\nRequirement already satisfied: tqdm<5.0.0,>=4.10.0 in /opt/conda/lib/python3.6/site-packages (from thinc<6.13.0,>=6.12.1->spacy>=2.0.18->fastai==1.0.47) (4.31.1)\nRequirement already satisfied: toolz>=0.8.0 in /opt/conda/lib/python3.6/site-packages (from cytoolz<0.10,>=0.9.0->thinc<6.13.0,>=6.12.1->spacy>=2.0.18->fastai==1.0.47) (0.9.0)\nInstalling collected packages: fastai\n  Found existing installation: fastai 1.0.50.post1\n    Uninstalling fastai-1.0.50.post1:\n      Successfully uninstalled fastai-1.0.50.post1\nSuccessfully installed fastai-1.0.47\n\n```\n\nIn [3]:\n\n```py\nimport time\nstart = time.time()\n\n```\n\nIn [4]:\n\n```py\nimport numpy as np \nimport pandas as pd \nfrom pytorchcv.model_provider import get_model as ptcv_get_model\n\n```\n\nIn [5]:\n\n```py\nfrom pathlib import Path\nfrom fastai import *\nfrom fastai.vision import *\nimport torch\n\n```\n\nIn [6]:\n\n```py\ndata_folder = Path(\"../input\")\n#data_folder.ls()\n\n```\n\nIn [7]:\n\n```py\ntrain_df = pd.read_csv(\"../input/train.csv\")\ntest_df = pd.read_csv(\"../input/sample_submission.csv\")\n\n```\n\nIn [8]:\n\n```py\ntest_img = ImageList.from_df(test_df, path=data_folder/'test', folder='test')\ntrfm = get_transforms(do_flip=True, flip_vert=True, max_rotate=10.0, max_zoom=1.1, max_lighting=0.2, max_warp=0.2, p_affine=0.75, p_lighting=0.75)\ntrain_img = (ImageList.from_df(train_df, path=data_folder/'train', folder='train')\n        .split_by_rand_pct(0.01)\n        .label_from_df()\n        .add_test(test_img)\n        .transform(trfm, size=128)\n        .databunch(path='.', bs=64, device= torch.device('cuda:0'))\n        .normalize(imagenet_stats)\n       )\n\n```\n\nIn [9]:\n\n```py\ndef md(f=None):\n    mdl = ptcv_get_model('condensenet74_c4_g4', pretrained=True)\n    mdl.features.final_pool = nn.AvgPool2d(kernel_size=7, stride=1, padding=3)\n    return mdl\n\n```\n\nIn [10]:\n\n```py\nlearn = cnn_learner(train_img, md, metrics=[error_rate, accuracy])\n\n```\n\n```\nDownloading /tmp/.torch/models/condensenet74_c4_g4-0828-5ba55049.pth.zip from https://github.com/osmr/imgclsmob/releases/download/v0.0.4/condensenet74_c4_g4-0828-5ba55049.pth.zip...\n\n```\n\nIn [11]:\n\n```py\n#learn.lr_find()\n#learn.recorder.plot()\n\n```\n\nIn [12]:\n\n```py\nlr = 3.5e-02\nlearn.fit_one_cycle(5, slice(lr))\n\n```\n\nTotal time: 04:54\n\n| epoch | train_loss | valid_loss | error_rate | accuracy | time |\n| --- | --- | --- | --- | --- | --- |\n| 0 | 0.083160 | 0.177766 | 0.040000 | 0.960000 | 01:06 |\n| 1 | 0.054930 | 0.036326 | 0.011429 | 0.988571 | 00:58 |\n| 2 | 0.040563 | 0.048152 | 0.011429 | 0.988571 | 00:58 |\n| 3 | 0.009462 | 0.001094 | 0.000000 | 1.000000 | 00:56 |\n| 4 | 0.004515 | 0.001212 | 0.000000 | 1.000000 | 00:54 |\n\nIn [13]:\n\n```py\npreds,_ = learn.get_preds(ds_type=DatasetType.Test)\n\n```\n\nIn [14]:\n\n```py\ntest_df.has_cactus = preds.numpy()[:, 0]\n\n```\n\nIn [15]:\n\n```py\ntest_df.to_csv('submission.csv', index=False)\n\n```\n\nIn [16]:\n\n```py\nend = time.time() \nprint(end - start)\n\n```\n\n```\n313.3328809738159\n\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/keras-transfer-vgg16.md",
    "content": "# Keras _Transfer_VGG16\n\n> Author: https://www.kaggle.com/ateplyuk\n\n> From: https://www.kaggle.com/ateplyuk/keras-transfer-vgg16\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\nIn [1]:\n\n```py\nimport cv2\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport json\nimport os\nfrom tqdm import tqdm, tqdm_notebook\nfrom keras.models import Sequential\nfrom keras.layers import Activation, Dropout, Flatten, Dense\nfrom keras.applications import VGG16\nfrom keras.optimizers import Adam\n\n```\n\n```\nUsing TensorFlow backend.\n\n```\n\nIn [2]:\n\n```py\ntrain_dir = \"../input/train/train/\"\ntest_dir = \"../input/test/test/\"\ntrain_df = pd.read_csv('../input/train.csv')\ntrain_df.head()\n\n```\n\nOut[2]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |\n| --- | --- | --- |\n| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |\n| --- | --- | --- |\n| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |\n| --- | --- | --- |\n| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |\n| --- | --- | --- |\n| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |\n| --- | --- | --- |\n\nIn [3]:\n\n```py\nim = cv2.imread(\"../input/train/train/01e30c0ba6e91343a12d2126fcafc0dd.jpg\")\nplt.imshow(im)\n\n```\n\nOut[3]:\n\n```\n<matplotlib.image.AxesImage at 0x7f884392a0f0>\n```\n\n![](keras-transfer-vgg16_files/__results___2_1.png)In [4]:\n\n```py\nvgg16_net = VGG16(weights='imagenet', \n                  include_top=False, \n                  input_shape=(32, 32, 3))\n\n```\n\n```\nDownloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n58892288/58889256 [==============================] - 2s 0us/step\n\n```\n\nIn [5]:\n\n```py\nvgg16_net.trainable = False\nvgg16_net.summary()\n\n```\n\n```\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_1 (InputLayer)         (None, 32, 32, 3)         0         \n_________________________________________________________________\nblock1_conv1 (Conv2D)        (None, 32, 32, 64)        1792      \n_________________________________________________________________\nblock1_conv2 (Conv2D)        (None, 32, 32, 64)        36928     \n_________________________________________________________________\nblock1_pool (MaxPooling2D)   (None, 16, 16, 64)        0         \n_________________________________________________________________\nblock2_conv1 (Conv2D)        (None, 16, 16, 128)       73856     \n_________________________________________________________________\nblock2_conv2 (Conv2D)        (None, 16, 16, 128)       147584    \n_________________________________________________________________\nblock2_pool (MaxPooling2D)   (None, 8, 8, 128)         0         \n_________________________________________________________________\nblock3_conv1 (Conv2D)        (None, 8, 8, 256)         295168    \n_________________________________________________________________\nblock3_conv2 (Conv2D)        (None, 8, 8, 256)         590080    \n_________________________________________________________________\nblock3_conv3 (Conv2D)        (None, 8, 8, 256)         590080    \n_________________________________________________________________\nblock3_pool (MaxPooling2D)   (None, 4, 4, 256)         0         \n_________________________________________________________________\nblock4_conv1 (Conv2D)        (None, 4, 4, 512)         1180160   \n_________________________________________________________________\nblock4_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n_________________________________________________________________\nblock4_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n_________________________________________________________________\nblock4_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n_________________________________________________________________\nblock5_conv1 (Conv2D)        (None, 2, 2, 512)         2359808   \n_________________________________________________________________\nblock5_conv2 (Conv2D)        (None, 2, 2, 512)         2359808   \n_________________________________________________________________\nblock5_conv3 (Conv2D)        (None, 2, 2, 512)         2359808   \n_________________________________________________________________\nblock5_pool (MaxPooling2D)   (None, 1, 1, 512)         0         \n=================================================================\nTotal params: 14,714,688\nTrainable params: 0\nNon-trainable params: 14,714,688\n_________________________________________________________________\n\n```\n\nIn [6]:\n\n```py\nmodel = Sequential()\nmodel.add(vgg16_net)\nmodel.add(Flatten())\nmodel.add(Dense(256))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(1))\nmodel.add(Activation('sigmoid'))\n\n```\n\nIn [7]:\n\n```py\nmodel.compile(loss='binary_crossentropy',\n              optimizer=Adam(lr=1e-5), \n              metrics=['accuracy'])\n\n```\n\nIn [8]:\n\n```py\nX_tr = []\nY_tr = []\nimges = train_df['id'].values\nfor img_id in tqdm_notebook(imges):\n    X_tr.append(cv2.imread(train_dir + img_id))    \n    Y_tr.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])  \nX_tr = np.asarray(X_tr)\nX_tr = X_tr.astype('float32')\nX_tr /= 255\nY_tr = np.asarray(Y_tr)\n\n```\n\nIn [9]:\n\n```py\nbatch_size = 32\nnb_epoch = 1000\n\n```\n\nIn [10]:\n\n```py\n%%time\n# Train model\nhistory = model.fit(X_tr, Y_tr,\n              batch_size=batch_size,\n              epochs=nb_epoch,\n              validation_split=0.1,\n              shuffle=True,\n              verbose=2)\n\n```\n\n```\nTrain on 15750 samples, validate on 1750 samples\nEpoch 1/1000\n - 6s - loss: 0.6634 - acc: 0.6057 - val_loss: 0.5174 - val_acc: 0.7469\nEpoch 2/1000\n - 4s - loss: 0.4968 - acc: 0.7640 - val_loss: 0.4371 - val_acc: 0.7491\nEpoch 3/1000\n - 4s - loss: 0.4248 - acc: 0.7969 - val_loss: 0.3778 - val_acc: 0.7846\nEpoch 4/1000\n - 4s - loss: 0.3735 - acc: 0.8302 - val_loss: 0.3338 - val_acc: 0.8429\nEpoch 5/1000\n - 4s - loss: 0.3326 - acc: 0.8596 - val_loss: 0.2985 - val_acc: 0.9057\nEpoch 6/1000\n - 4s - loss: 0.3004 - acc: 0.8844 - val_loss: 0.2719 - val_acc: 0.9200\nEpoch 7/1000\n - 4s - loss: 0.2755 - acc: 0.8990 - val_loss: 0.2502 - val_acc: 0.9326\nEpoch 8/1000\n - 4s - loss: 0.2560 - acc: 0.9116 - val_loss: 0.2326 - val_acc: 0.9383\nEpoch 9/1000\n - 4s - loss: 0.2406 - acc: 0.9180 - val_loss: 0.2184 - val_acc: 0.9400\nEpoch 10/1000\n - 4s - loss: 0.2257 - acc: 0.9233 - val_loss: 0.2064 - val_acc: 0.9423\nEpoch 11/1000\n - 4s - loss: 0.2124 - acc: 0.9278 - val_loss: 0.1955 - val_acc: 0.9423\nEpoch 12/1000\n - 4s - loss: 0.2035 - acc: 0.9333 - val_loss: 0.1866 - val_acc: 0.9469\nEpoch 13/1000\n - 4s - loss: 0.1951 - acc: 0.9349 - val_loss: 0.1787 - val_acc: 0.9491\nEpoch 14/1000\n - 4s - loss: 0.1870 - acc: 0.9342 - val_loss: 0.1723 - val_acc: 0.9503\nEpoch 15/1000\n - 4s - loss: 0.1809 - acc: 0.9382 - val_loss: 0.1664 - val_acc: 0.9509\nEpoch 16/1000\n - 4s - loss: 0.1760 - acc: 0.9385 - val_loss: 0.1612 - val_acc: 0.9526\nEpoch 17/1000\n - 4s - loss: 0.1700 - acc: 0.9423 - val_loss: 0.1572 - val_acc: 0.9514\nEpoch 18/1000\n - 4s - loss: 0.1654 - acc: 0.9448 - val_loss: 0.1524 - val_acc: 0.9531\nEpoch 19/1000\n - 4s - loss: 0.1617 - acc: 0.9438 - val_loss: 0.1491 - val_acc: 0.9543\nEpoch 20/1000\n - 4s - loss: 0.1576 - acc: 0.9448 - val_loss: 0.1459 - val_acc: 0.9549\nEpoch 21/1000\n - 4s - loss: 0.1532 - acc: 0.9462 - val_loss: 0.1424 - val_acc: 0.9543\nEpoch 22/1000\n - 4s - loss: 0.1501 - acc: 0.9495 - val_loss: 0.1404 - val_acc: 0.9549\nEpoch 23/1000\n - 4s - loss: 0.1476 - acc: 0.9474 - val_loss: 0.1371 - val_acc: 0.9543\nEpoch 24/1000\n - 4s - loss: 0.1454 - acc: 0.9477 - val_loss: 0.1359 - val_acc: 0.9549\nEpoch 25/1000\n - 4s - loss: 0.1437 - acc: 0.9486 - val_loss: 0.1330 - val_acc: 0.9549\nEpoch 26/1000\n - 4s - loss: 0.1424 - acc: 0.9490 - val_loss: 0.1306 - val_acc: 0.9543\nEpoch 27/1000\n - 4s - loss: 0.1399 - acc: 0.9501 - val_loss: 0.1291 - val_acc: 0.9554\nEpoch 28/1000\n - 4s - loss: 0.1371 - acc: 0.9505 - val_loss: 0.1274 - val_acc: 0.9549\nEpoch 29/1000\n - 4s - loss: 0.1369 - acc: 0.9503 - val_loss: 0.1262 - val_acc: 0.9566\nEpoch 30/1000\n - 4s - loss: 0.1338 - acc: 0.9514 - val_loss: 0.1241 - val_acc: 0.9577\nEpoch 31/1000\n - 4s - loss: 0.1328 - acc: 0.9514 - val_loss: 0.1224 - val_acc: 0.9577\nEpoch 32/1000\n - 4s - loss: 0.1324 - acc: 0.9520 - val_loss: 0.1213 - val_acc: 0.9589\nEpoch 33/1000\n - 4s - loss: 0.1290 - acc: 0.9540 - val_loss: 0.1202 - val_acc: 0.9589\nEpoch 34/1000\n - 4s - loss: 0.1287 - acc: 0.9532 - val_loss: 0.1188 - val_acc: 0.9583\nEpoch 35/1000\n - 4s - loss: 0.1276 - acc: 0.9528 - val_loss: 0.1177 - val_acc: 0.9583\nEpoch 36/1000\n - 4s - loss: 0.1277 - acc: 0.9528 - val_loss: 0.1171 - val_acc: 0.9589\nEpoch 37/1000\n - 4s - loss: 0.1255 - acc: 0.9545 - val_loss: 0.1158 - val_acc: 0.9589\nEpoch 38/1000\n - 4s - loss: 0.1227 - acc: 0.9561 - val_loss: 0.1152 - val_acc: 0.9594\nEpoch 39/1000\n - 4s - loss: 0.1230 - acc: 0.9554 - val_loss: 0.1137 - val_acc: 0.9583\nEpoch 40/1000\n - 4s - loss: 0.1209 - acc: 0.9559 - val_loss: 0.1127 - val_acc: 0.9583\nEpoch 41/1000\n - 4s - loss: 0.1202 - acc: 0.9563 - val_loss: 0.1126 - val_acc: 0.9606\nEpoch 42/1000\n - 4s - loss: 0.1198 - acc: 0.9561 - val_loss: 0.1117 - val_acc: 0.9606\nEpoch 43/1000\n - 4s - loss: 0.1184 - acc: 0.9565 - val_loss: 0.1101 - val_acc: 0.9589\nEpoch 44/1000\n - 4s - loss: 0.1165 - acc: 0.9583 - val_loss: 0.1096 - val_acc: 0.9589\nEpoch 45/1000\n - 4s - loss: 0.1179 - acc: 0.9574 - val_loss: 0.1086 - val_acc: 0.9594\nEpoch 46/1000\n - 4s - loss: 0.1160 - acc: 0.9580 - val_loss: 0.1081 - val_acc: 0.9594\nEpoch 47/1000\n - 4s - loss: 0.1156 - acc: 0.9573 - val_loss: 0.1073 - val_acc: 0.9600\nEpoch 48/1000\n - 4s - loss: 0.1144 - acc: 0.9570 - val_loss: 0.1074 - val_acc: 0.9629\nEpoch 49/1000\n - 4s - loss: 0.1145 - acc: 0.9575 - val_loss: 0.1058 - val_acc: 0.9594\nEpoch 50/1000\n - 4s - loss: 0.1135 - acc: 0.9589 - val_loss: 0.1053 - val_acc: 0.9617\nEpoch 51/1000\n - 4s - loss: 0.1127 - acc: 0.9590 - val_loss: 0.1047 - val_acc: 0.9611\nEpoch 52/1000\n - 4s - loss: 0.1119 - acc: 0.9582 - val_loss: 0.1046 - val_acc: 0.9634\nEpoch 53/1000\n - 4s - loss: 0.1110 - acc: 0.9596 - val_loss: 0.1039 - val_acc: 0.9629\nEpoch 54/1000\n - 4s - loss: 0.1108 - acc: 0.9593 - val_loss: 0.1032 - val_acc: 0.9623\nEpoch 55/1000\n - 4s - loss: 0.1097 - acc: 0.9593 - val_loss: 0.1024 - val_acc: 0.9617\nEpoch 56/1000\n - 4s - loss: 0.1089 - acc: 0.9585 - val_loss: 0.1021 - val_acc: 0.9623\nEpoch 57/1000\n - 4s - loss: 0.1074 - acc: 0.9611 - val_loss: 0.1018 - val_acc: 0.9640\nEpoch 58/1000\n - 4s - loss: 0.1082 - acc: 0.9592 - val_loss: 0.1008 - val_acc: 0.9617\nEpoch 59/1000\n - 4s - loss: 0.1076 - acc: 0.9608 - val_loss: 0.1006 - val_acc: 0.9623\nEpoch 60/1000\n - 4s - loss: 0.1065 - acc: 0.9615 - val_loss: 0.1003 - val_acc: 0.9651\nEpoch 61/1000\n - 4s - loss: 0.1060 - acc: 0.9605 - val_loss: 0.0995 - val_acc: 0.9623\nEpoch 62/1000\n - 4s - loss: 0.1051 - acc: 0.9615 - val_loss: 0.0990 - val_acc: 0.9629\nEpoch 63/1000\n - 4s - loss: 0.1059 - acc: 0.9609 - val_loss: 0.0991 - val_acc: 0.9657\nEpoch 64/1000\n - 4s - loss: 0.1042 - acc: 0.9622 - val_loss: 0.0989 - val_acc: 0.9657\nEpoch 65/1000\n - 4s - loss: 0.1040 - acc: 0.9619 - val_loss: 0.0981 - val_acc: 0.9646\nEpoch 66/1000\n - 4s - loss: 0.1040 - acc: 0.9630 - val_loss: 0.0973 - val_acc: 0.9640\nEpoch 67/1000\n - 4s - loss: 0.1024 - acc: 0.9624 - val_loss: 0.0973 - val_acc: 0.9646\nEpoch 68/1000\n - 4s - loss: 0.1019 - acc: 0.9634 - val_loss: 0.0967 - val_acc: 0.9640\nEpoch 69/1000\n - 4s - loss: 0.1022 - acc: 0.9634 - val_loss: 0.0966 - val_acc: 0.9651\nEpoch 70/1000\n - 4s - loss: 0.1004 - acc: 0.9641 - val_loss: 0.0955 - val_acc: 0.9646\nEpoch 71/1000\n - 4s - loss: 0.1021 - acc: 0.9622 - val_loss: 0.0952 - val_acc: 0.9640\nEpoch 72/1000\n - 4s - loss: 0.1025 - acc: 0.9625 - val_loss: 0.0947 - val_acc: 0.9640\nEpoch 73/1000\n - 4s - loss: 0.1002 - acc: 0.9627 - val_loss: 0.0943 - val_acc: 0.9646\nEpoch 74/1000\n - 4s - loss: 0.0998 - acc: 0.9631 - val_loss: 0.0941 - val_acc: 0.9640\nEpoch 75/1000\n - 4s - loss: 0.1006 - acc: 0.9632 - val_loss: 0.0943 - val_acc: 0.9640\nEpoch 76/1000\n - 4s - loss: 0.0991 - acc: 0.9636 - val_loss: 0.0935 - val_acc: 0.9651\nEpoch 77/1000\n - 4s - loss: 0.0991 - acc: 0.9650 - val_loss: 0.0931 - val_acc: 0.9646\nEpoch 78/1000\n - 4s - loss: 0.0983 - acc: 0.9642 - val_loss: 0.0935 - val_acc: 0.9651\nEpoch 79/1000\n - 4s - loss: 0.0983 - acc: 0.9636 - val_loss: 0.0926 - val_acc: 0.9651\nEpoch 80/1000\n - 4s - loss: 0.0965 - acc: 0.9651 - val_loss: 0.0921 - val_acc: 0.9651\nEpoch 81/1000\n - 4s - loss: 0.0970 - acc: 0.9630 - val_loss: 0.0920 - val_acc: 0.9646\nEpoch 82/1000\n - 4s - loss: 0.0962 - acc: 0.9648 - val_loss: 0.0916 - val_acc: 0.9651\nEpoch 83/1000\n - 4s - loss: 0.0984 - acc: 0.9632 - val_loss: 0.0915 - val_acc: 0.9646\nEpoch 84/1000\n - 4s - loss: 0.0980 - acc: 0.9639 - val_loss: 0.0912 - val_acc: 0.9646\nEpoch 85/1000\n - 4s - loss: 0.0964 - acc: 0.9655 - val_loss: 0.0910 - val_acc: 0.9651\nEpoch 86/1000\n - 4s - loss: 0.0956 - acc: 0.9650 - val_loss: 0.0910 - val_acc: 0.9651\nEpoch 87/1000\n - 4s - loss: 0.0942 - acc: 0.9643 - val_loss: 0.0904 - val_acc: 0.9651\nEpoch 88/1000\n - 4s - loss: 0.0953 - acc: 0.9648 - val_loss: 0.0901 - val_acc: 0.9651\nEpoch 89/1000\n - 4s - loss: 0.0944 - acc: 0.9658 - val_loss: 0.0898 - val_acc: 0.9651\nEpoch 90/1000\n - 4s - loss: 0.0945 - acc: 0.9651 - val_loss: 0.0895 - val_acc: 0.9651\nEpoch 91/1000\n - 4s - loss: 0.0936 - acc: 0.9657 - val_loss: 0.0888 - val_acc: 0.9657\nEpoch 92/1000\n - 4s - loss: 0.0924 - acc: 0.9658 - val_loss: 0.0891 - val_acc: 0.9646\nEpoch 93/1000\n - 4s - loss: 0.0924 - acc: 0.9658 - val_loss: 0.0889 - val_acc: 0.9657\nEpoch 94/1000\n - 4s - loss: 0.0935 - acc: 0.9655 - val_loss: 0.0883 - val_acc: 0.9663\nEpoch 95/1000\n - 4s - loss: 0.0933 - acc: 0.9653 - val_loss: 0.0882 - val_acc: 0.9663\nEpoch 96/1000\n - 4s - loss: 0.0923 - acc: 0.9654 - val_loss: 0.0884 - val_acc: 0.9669\nEpoch 97/1000\n - 4s - loss: 0.0916 - acc: 0.9648 - val_loss: 0.0876 - val_acc: 0.9663\nEpoch 98/1000\n - 4s - loss: 0.0920 - acc: 0.9659 - val_loss: 0.0877 - val_acc: 0.9669\nEpoch 99/1000\n - 4s - loss: 0.0923 - acc: 0.9661 - val_loss: 0.0872 - val_acc: 0.9669\nEpoch 100/1000\n - 4s - loss: 0.0909 - acc: 0.9674 - val_loss: 0.0871 - val_acc: 0.9669\nEpoch 101/1000\n - 4s - loss: 0.0907 - acc: 0.9661 - val_loss: 0.0866 - val_acc: 0.9669\nEpoch 102/1000\n - 4s - loss: 0.0906 - acc: 0.9668 - val_loss: 0.0863 - val_acc: 0.9674\nEpoch 103/1000\n - 4s - loss: 0.0903 - acc: 0.9674 - val_loss: 0.0861 - val_acc: 0.9669\nEpoch 104/1000\n - 4s - loss: 0.0893 - acc: 0.9665 - val_loss: 0.0861 - val_acc: 0.9669\nEpoch 105/1000\n - 4s - loss: 0.0897 - acc: 0.9663 - val_loss: 0.0858 - val_acc: 0.9669\nEpoch 106/1000\n - 4s - loss: 0.0894 - acc: 0.9672 - val_loss: 0.0855 - val_acc: 0.9674\nEpoch 107/1000\n - 4s - loss: 0.0889 - acc: 0.9678 - val_loss: 0.0856 - val_acc: 0.9669\nEpoch 108/1000\n - 4s - loss: 0.0886 - acc: 0.9672 - val_loss: 0.0856 - val_acc: 0.9669\nEpoch 109/1000\n - 4s - loss: 0.0882 - acc: 0.9681 - val_loss: 0.0854 - val_acc: 0.9669\nEpoch 110/1000\n - 4s - loss: 0.0889 - acc: 0.9671 - val_loss: 0.0851 - val_acc: 0.9669\nEpoch 111/1000\n - 4s - loss: 0.0877 - acc: 0.9693 - val_loss: 0.0846 - val_acc: 0.9686\nEpoch 112/1000\n - 4s - loss: 0.0876 - acc: 0.9674 - val_loss: 0.0844 - val_acc: 0.9680\nEpoch 113/1000\n - 4s - loss: 0.0860 - acc: 0.9681 - val_loss: 0.0848 - val_acc: 0.9686\nEpoch 114/1000\n - 4s - loss: 0.0866 - acc: 0.9684 - val_loss: 0.0840 - val_acc: 0.9686\nEpoch 115/1000\n - 4s - loss: 0.0870 - acc: 0.9681 - val_loss: 0.0837 - val_acc: 0.9686\nEpoch 116/1000\n - 4s - loss: 0.0861 - acc: 0.9681 - val_loss: 0.0836 - val_acc: 0.9686\nEpoch 117/1000\n - 4s - loss: 0.0855 - acc: 0.9681 - val_loss: 0.0838 - val_acc: 0.9686\nEpoch 118/1000\n - 4s - loss: 0.0859 - acc: 0.9684 - val_loss: 0.0832 - val_acc: 0.9686\nEpoch 119/1000\n - 4s - loss: 0.0859 - acc: 0.9688 - val_loss: 0.0830 - val_acc: 0.9686\nEpoch 120/1000\n - 4s - loss: 0.0858 - acc: 0.9674 - val_loss: 0.0830 - val_acc: 0.9691\nEpoch 121/1000\n - 4s - loss: 0.0843 - acc: 0.9691 - val_loss: 0.0829 - val_acc: 0.9686\nEpoch 122/1000\n - 4s - loss: 0.0853 - acc: 0.9684 - val_loss: 0.0827 - val_acc: 0.9691\nEpoch 123/1000\n - 4s - loss: 0.0851 - acc: 0.9688 - val_loss: 0.0826 - val_acc: 0.9691\nEpoch 124/1000\n - 4s - loss: 0.0845 - acc: 0.9689 - val_loss: 0.0822 - val_acc: 0.9691\nEpoch 125/1000\n - 4s - loss: 0.0848 - acc: 0.9684 - val_loss: 0.0820 - val_acc: 0.9691\nEpoch 126/1000\n - 4s - loss: 0.0831 - acc: 0.9688 - val_loss: 0.0820 - val_acc: 0.9691\nEpoch 127/1000\n - 4s - loss: 0.0850 - acc: 0.9677 - val_loss: 0.0817 - val_acc: 0.9691\nEpoch 128/1000\n - 4s - loss: 0.0846 - acc: 0.9685 - val_loss: 0.0815 - val_acc: 0.9691\nEpoch 129/1000\n - 4s - loss: 0.0836 - acc: 0.9694 - val_loss: 0.0817 - val_acc: 0.9691\nEpoch 130/1000\n - 4s - loss: 0.0828 - acc: 0.9697 - val_loss: 0.0811 - val_acc: 0.9691\nEpoch 131/1000\n - 4s - loss: 0.0830 - acc: 0.9698 - val_loss: 0.0809 - val_acc: 0.9691\nEpoch 132/1000\n - 4s - loss: 0.0825 - acc: 0.9700 - val_loss: 0.0810 - val_acc: 0.9691\nEpoch 133/1000\n - 4s - loss: 0.0822 - acc: 0.9699 - val_loss: 0.0805 - val_acc: 0.9691\nEpoch 134/1000\n - 4s - loss: 0.0823 - acc: 0.9701 - val_loss: 0.0806 - val_acc: 0.9691\nEpoch 135/1000\n - 4s - loss: 0.0822 - acc: 0.9694 - val_loss: 0.0802 - val_acc: 0.9691\nEpoch 136/1000\n - 4s - loss: 0.0816 - acc: 0.9711 - val_loss: 0.0807 - val_acc: 0.9691\nEpoch 137/1000\n - 4s - loss: 0.0828 - acc: 0.9690 - val_loss: 0.0804 - val_acc: 0.9691\nEpoch 138/1000\n - 4s - loss: 0.0820 - acc: 0.9693 - val_loss: 0.0801 - val_acc: 0.9691\nEpoch 139/1000\n - 4s - loss: 0.0825 - acc: 0.9697 - val_loss: 0.0798 - val_acc: 0.9691\nEpoch 140/1000\n - 4s - loss: 0.0820 - acc: 0.9690 - val_loss: 0.0797 - val_acc: 0.9691\nEpoch 141/1000\n - 4s - loss: 0.0810 - acc: 0.9697 - val_loss: 0.0796 - val_acc: 0.9691\nEpoch 142/1000\n - 4s - loss: 0.0804 - acc: 0.9707 - val_loss: 0.0797 - val_acc: 0.9691\nEpoch 143/1000\n - 4s - loss: 0.0812 - acc: 0.9697 - val_loss: 0.0794 - val_acc: 0.9691\nEpoch 144/1000\n - 4s - loss: 0.0805 - acc: 0.9695 - val_loss: 0.0791 - val_acc: 0.9686\nEpoch 145/1000\n - 4s - loss: 0.0804 - acc: 0.9709 - val_loss: 0.0794 - val_acc: 0.9691\nEpoch 146/1000\n - 4s - loss: 0.0799 - acc: 0.9706 - val_loss: 0.0789 - val_acc: 0.9691\nEpoch 147/1000\n - 4s - loss: 0.0790 - acc: 0.9714 - val_loss: 0.0788 - val_acc: 0.9691\nEpoch 148/1000\n - 4s - loss: 0.0794 - acc: 0.9703 - val_loss: 0.0787 - val_acc: 0.9691\nEpoch 149/1000\n - 4s - loss: 0.0792 - acc: 0.9702 - val_loss: 0.0785 - val_acc: 0.9691\nEpoch 150/1000\n - 4s - loss: 0.0802 - acc: 0.9709 - val_loss: 0.0783 - val_acc: 0.9691\nEpoch 151/1000\n - 4s - loss: 0.0795 - acc: 0.9711 - val_loss: 0.0781 - val_acc: 0.9691\nEpoch 152/1000\n - 4s - loss: 0.0778 - acc: 0.9706 - val_loss: 0.0780 - val_acc: 0.9691\nEpoch 153/1000\n - 4s - loss: 0.0772 - acc: 0.9716 - val_loss: 0.0782 - val_acc: 0.9691\nEpoch 154/1000\n - 4s - loss: 0.0783 - acc: 0.9710 - val_loss: 0.0779 - val_acc: 0.9691\nEpoch 155/1000\n - 4s - loss: 0.0789 - acc: 0.9704 - val_loss: 0.0779 - val_acc: 0.9691\nEpoch 156/1000\n - 4s - loss: 0.0782 - acc: 0.9708 - val_loss: 0.0781 - val_acc: 0.9703\nEpoch 157/1000\n - 4s - loss: 0.0777 - acc: 0.9712 - val_loss: 0.0774 - val_acc: 0.9691\nEpoch 158/1000\n - 4s - loss: 0.0775 - acc: 0.9715 - val_loss: 0.0779 - val_acc: 0.9703\nEpoch 159/1000\n - 4s - loss: 0.0775 - acc: 0.9722 - val_loss: 0.0775 - val_acc: 0.9697\nEpoch 160/1000\n - 4s - loss: 0.0767 - acc: 0.9719 - val_loss: 0.0770 - val_acc: 0.9686\nEpoch 161/1000\n - 4s - loss: 0.0773 - acc: 0.9712 - val_loss: 0.0772 - val_acc: 0.9703\nEpoch 162/1000\n - 4s - loss: 0.0770 - acc: 0.9716 - val_loss: 0.0770 - val_acc: 0.9697\nEpoch 163/1000\n - 4s - loss: 0.0765 - acc: 0.9714 - val_loss: 0.0770 - val_acc: 0.9703\nEpoch 164/1000\n - 4s - loss: 0.0779 - acc: 0.9707 - val_loss: 0.0767 - val_acc: 0.9691\nEpoch 165/1000\n - 4s - loss: 0.0768 - acc: 0.9712 - val_loss: 0.0767 - val_acc: 0.9697\nEpoch 166/1000\n - 4s - loss: 0.0765 - acc: 0.9719 - val_loss: 0.0763 - val_acc: 0.9686\nEpoch 167/1000\n - 4s - loss: 0.0755 - acc: 0.9714 - val_loss: 0.0765 - val_acc: 0.9703\nEpoch 168/1000\n - 4s - loss: 0.0755 - acc: 0.9731 - val_loss: 0.0762 - val_acc: 0.9691\nEpoch 169/1000\n - 4s - loss: 0.0764 - acc: 0.9712 - val_loss: 0.0763 - val_acc: 0.9703\nEpoch 170/1000\n - 4s - loss: 0.0759 - acc: 0.9721 - val_loss: 0.0760 - val_acc: 0.9697\nEpoch 171/1000\n - 4s - loss: 0.0756 - acc: 0.9723 - val_loss: 0.0761 - val_acc: 0.9703\nEpoch 172/1000\n - 4s - loss: 0.0753 - acc: 0.9719 - val_loss: 0.0758 - val_acc: 0.9703\nEpoch 173/1000\n - 4s - loss: 0.0752 - acc: 0.9721 - val_loss: 0.0761 - val_acc: 0.9703\nEpoch 174/1000\n - 4s - loss: 0.0757 - acc: 0.9712 - val_loss: 0.0755 - val_acc: 0.9703\nEpoch 175/1000\n - 4s - loss: 0.0741 - acc: 0.9728 - val_loss: 0.0755 - val_acc: 0.9703\nEpoch 176/1000\n - 4s - loss: 0.0753 - acc: 0.9721 - val_loss: 0.0755 - val_acc: 0.9703\nEpoch 177/1000\n - 4s - loss: 0.0760 - acc: 0.9722 - val_loss: 0.0752 - val_acc: 0.9697\nEpoch 178/1000\n - 4s - loss: 0.0748 - acc: 0.9729 - val_loss: 0.0752 - val_acc: 0.9703\nEpoch 179/1000\n - 4s - loss: 0.0745 - acc: 0.9725 - val_loss: 0.0751 - val_acc: 0.9697\nEpoch 180/1000\n - 4s - loss: 0.0744 - acc: 0.9723 - val_loss: 0.0751 - val_acc: 0.9703\nEpoch 181/1000\n - 4s - loss: 0.0741 - acc: 0.9725 - val_loss: 0.0750 - val_acc: 0.9703\nEpoch 182/1000\n - 4s - loss: 0.0739 - acc: 0.9723 - val_loss: 0.0747 - val_acc: 0.9703\nEpoch 183/1000\n - 4s - loss: 0.0757 - acc: 0.9714 - val_loss: 0.0749 - val_acc: 0.9703\nEpoch 184/1000\n - 4s - loss: 0.0739 - acc: 0.9734 - val_loss: 0.0749 - val_acc: 0.9703\nEpoch 185/1000\n - 4s - loss: 0.0730 - acc: 0.9722 - val_loss: 0.0746 - val_acc: 0.9697\nEpoch 186/1000\n - 4s - loss: 0.0739 - acc: 0.9735 - val_loss: 0.0744 - val_acc: 0.9703\nEpoch 187/1000\n - 4s - loss: 0.0732 - acc: 0.9728 - val_loss: 0.0743 - val_acc: 0.9703\nEpoch 188/1000\n - 4s - loss: 0.0735 - acc: 0.9732 - val_loss: 0.0743 - val_acc: 0.9703\nEpoch 189/1000\n - 4s - loss: 0.0729 - acc: 0.9721 - val_loss: 0.0744 - val_acc: 0.9703\nEpoch 190/1000\n - 4s - loss: 0.0739 - acc: 0.9731 - val_loss: 0.0743 - val_acc: 0.9703\nEpoch 191/1000\n - 4s - loss: 0.0725 - acc: 0.9733 - val_loss: 0.0741 - val_acc: 0.9697\nEpoch 192/1000\n - 4s - loss: 0.0731 - acc: 0.9731 - val_loss: 0.0743 - val_acc: 0.9709\nEpoch 193/1000\n - 4s - loss: 0.0722 - acc: 0.9731 - val_loss: 0.0741 - val_acc: 0.9703\nEpoch 194/1000\n - 4s - loss: 0.0717 - acc: 0.9736 - val_loss: 0.0741 - val_acc: 0.9709\nEpoch 195/1000\n - 4s - loss: 0.0728 - acc: 0.9735 - val_loss: 0.0738 - val_acc: 0.9703\nEpoch 196/1000\n - 4s - loss: 0.0722 - acc: 0.9734 - val_loss: 0.0737 - val_acc: 0.9703\nEpoch 197/1000\n - 4s - loss: 0.0728 - acc: 0.9727 - val_loss: 0.0739 - val_acc: 0.9709\nEpoch 198/1000\n - 4s - loss: 0.0720 - acc: 0.9736 - val_loss: 0.0735 - val_acc: 0.9703\nEpoch 199/1000\n - 4s - loss: 0.0717 - acc: 0.9742 - val_loss: 0.0734 - val_acc: 0.9697\nEpoch 200/1000\n - 4s - loss: 0.0712 - acc: 0.9737 - val_loss: 0.0735 - val_acc: 0.9709\nEpoch 201/1000\n - 4s - loss: 0.0715 - acc: 0.9742 - val_loss: 0.0733 - val_acc: 0.9697\nEpoch 202/1000\n - 4s - loss: 0.0716 - acc: 0.9742 - val_loss: 0.0732 - val_acc: 0.9697\nEpoch 203/1000\n - 4s - loss: 0.0709 - acc: 0.9742 - val_loss: 0.0734 - val_acc: 0.9709\nEpoch 204/1000\n - 4s - loss: 0.0719 - acc: 0.9726 - val_loss: 0.0730 - val_acc: 0.9697\nEpoch 205/1000\n - 4s - loss: 0.0704 - acc: 0.9725 - val_loss: 0.0729 - val_acc: 0.9697\nEpoch 206/1000\n - 5s - loss: 0.0710 - acc: 0.9740 - val_loss: 0.0731 - val_acc: 0.9709\nEpoch 207/1000\n - 4s - loss: 0.0714 - acc: 0.9731 - val_loss: 0.0730 - val_acc: 0.9709\nEpoch 208/1000\n - 4s - loss: 0.0707 - acc: 0.9739 - val_loss: 0.0728 - val_acc: 0.9709\nEpoch 209/1000\n - 4s - loss: 0.0709 - acc: 0.9732 - val_loss: 0.0726 - val_acc: 0.9709\nEpoch 210/1000\n - 4s - loss: 0.0693 - acc: 0.9745 - val_loss: 0.0727 - val_acc: 0.9709\nEpoch 211/1000\n - 4s - loss: 0.0715 - acc: 0.9731 - val_loss: 0.0728 - val_acc: 0.9709\nEpoch 212/1000\n - 4s - loss: 0.0696 - acc: 0.9739 - val_loss: 0.0726 - val_acc: 0.9709\nEpoch 213/1000\n - 4s - loss: 0.0699 - acc: 0.9743 - val_loss: 0.0725 - val_acc: 0.9709\nEpoch 214/1000\n - 4s - loss: 0.0698 - acc: 0.9748 - val_loss: 0.0723 - val_acc: 0.9709\nEpoch 215/1000\n - 4s - loss: 0.0695 - acc: 0.9742 - val_loss: 0.0724 - val_acc: 0.9709\nEpoch 216/1000\n - 4s - loss: 0.0694 - acc: 0.9744 - val_loss: 0.0721 - val_acc: 0.9714\nEpoch 217/1000\n - 4s - loss: 0.0700 - acc: 0.9739 - val_loss: 0.0722 - val_acc: 0.9714\nEpoch 218/1000\n - 4s - loss: 0.0679 - acc: 0.9747 - val_loss: 0.0720 - val_acc: 0.9714\nEpoch 219/1000\n - 4s - loss: 0.0694 - acc: 0.9749 - val_loss: 0.0720 - val_acc: 0.9709\nEpoch 220/1000\n - 4s - loss: 0.0698 - acc: 0.9744 - val_loss: 0.0719 - val_acc: 0.9709\nEpoch 221/1000\n - 4s - loss: 0.0689 - acc: 0.9745 - val_loss: 0.0717 - val_acc: 0.9720\nEpoch 222/1000\n - 4s - loss: 0.0680 - acc: 0.9747 - val_loss: 0.0716 - val_acc: 0.9720\nEpoch 223/1000\n - 4s - loss: 0.0686 - acc: 0.9751 - val_loss: 0.0715 - val_acc: 0.9720\nEpoch 224/1000\n - 4s - loss: 0.0678 - acc: 0.9759 - val_loss: 0.0715 - val_acc: 0.9720\nEpoch 225/1000\n - 4s - loss: 0.0688 - acc: 0.9752 - val_loss: 0.0714 - val_acc: 0.9720\nEpoch 226/1000\n - 4s - loss: 0.0680 - acc: 0.9737 - val_loss: 0.0714 - val_acc: 0.9720\nEpoch 227/1000\n - 4s - loss: 0.0694 - acc: 0.9745 - val_loss: 0.0714 - val_acc: 0.9720\nEpoch 228/1000\n - 4s - loss: 0.0694 - acc: 0.9747 - val_loss: 0.0714 - val_acc: 0.9714\nEpoch 229/1000\n - 4s - loss: 0.0690 - acc: 0.9754 - val_loss: 0.0712 - val_acc: 0.9720\nEpoch 230/1000\n - 4s - loss: 0.0682 - acc: 0.9745 - val_loss: 0.0715 - val_acc: 0.9714\nEpoch 231/1000\n - 4s - loss: 0.0675 - acc: 0.9756 - val_loss: 0.0710 - val_acc: 0.9720\nEpoch 232/1000\n - 4s - loss: 0.0690 - acc: 0.9750 - val_loss: 0.0711 - val_acc: 0.9720\nEpoch 233/1000\n - 4s - loss: 0.0673 - acc: 0.9752 - val_loss: 0.0709 - val_acc: 0.9720\nEpoch 234/1000\n - 4s - loss: 0.0670 - acc: 0.9764 - val_loss: 0.0708 - val_acc: 0.9720\nEpoch 235/1000\n - 4s - loss: 0.0669 - acc: 0.9752 - val_loss: 0.0708 - val_acc: 0.9726\nEpoch 236/1000\n - 4s - loss: 0.0673 - acc: 0.9754 - val_loss: 0.0709 - val_acc: 0.9714\nEpoch 237/1000\n - 4s - loss: 0.0672 - acc: 0.9752 - val_loss: 0.0707 - val_acc: 0.9720\nEpoch 238/1000\n - 4s - loss: 0.0672 - acc: 0.9747 - val_loss: 0.0706 - val_acc: 0.9720\nEpoch 239/1000\n - 4s - loss: 0.0662 - acc: 0.9754 - val_loss: 0.0706 - val_acc: 0.9720\nEpoch 240/1000\n - 4s - loss: 0.0669 - acc: 0.9752 - val_loss: 0.0705 - val_acc: 0.9720\nEpoch 241/1000\n - 4s - loss: 0.0662 - acc: 0.9756 - val_loss: 0.0704 - val_acc: 0.9726\nEpoch 242/1000\n - 4s - loss: 0.0667 - acc: 0.9755 - val_loss: 0.0705 - val_acc: 0.9720\nEpoch 243/1000\n - 4s - loss: 0.0663 - acc: 0.9750 - val_loss: 0.0705 - val_acc: 0.9714\nEpoch 244/1000\n - 4s - loss: 0.0654 - acc: 0.9748 - val_loss: 0.0706 - val_acc: 0.9720\nEpoch 245/1000\n - 4s - loss: 0.0666 - acc: 0.9757 - val_loss: 0.0702 - val_acc: 0.9726\nEpoch 246/1000\n - 4s - loss: 0.0662 - acc: 0.9761 - val_loss: 0.0702 - val_acc: 0.9726\nEpoch 247/1000\n - 4s - loss: 0.0671 - acc: 0.9749 - val_loss: 0.0701 - val_acc: 0.9731\nEpoch 248/1000\n - 4s - loss: 0.0660 - acc: 0.9756 - val_loss: 0.0706 - val_acc: 0.9720\nEpoch 249/1000\n - 4s - loss: 0.0656 - acc: 0.9759 - val_loss: 0.0700 - val_acc: 0.9731\nEpoch 250/1000\n - 4s - loss: 0.0670 - acc: 0.9754 - val_loss: 0.0699 - val_acc: 0.9731\nEpoch 251/1000\n - 4s - loss: 0.0653 - acc: 0.9757 - val_loss: 0.0698 - val_acc: 0.9726\nEpoch 252/1000\n - 4s - loss: 0.0667 - acc: 0.9749 - val_loss: 0.0700 - val_acc: 0.9720\nEpoch 253/1000\n - 4s - loss: 0.0651 - acc: 0.9756 - val_loss: 0.0699 - val_acc: 0.9731\nEpoch 254/1000\n - 4s - loss: 0.0667 - acc: 0.9765 - val_loss: 0.0698 - val_acc: 0.9731\nEpoch 255/1000\n - 4s - loss: 0.0645 - acc: 0.9761 - val_loss: 0.0699 - val_acc: 0.9720\nEpoch 256/1000\n - 4s - loss: 0.0657 - acc: 0.9759 - val_loss: 0.0697 - val_acc: 0.9720\nEpoch 257/1000\n - 4s - loss: 0.0653 - acc: 0.9760 - val_loss: 0.0695 - val_acc: 0.9731\nEpoch 258/1000\n - 4s - loss: 0.0640 - acc: 0.9759 - val_loss: 0.0695 - val_acc: 0.9731\nEpoch 259/1000\n - 4s - loss: 0.0648 - acc: 0.9766 - val_loss: 0.0697 - val_acc: 0.9726\nEpoch 260/1000\n - 4s - loss: 0.0641 - acc: 0.9771 - val_loss: 0.0694 - val_acc: 0.9731\nEpoch 261/1000\n - 4s - loss: 0.0650 - acc: 0.9756 - val_loss: 0.0693 - val_acc: 0.9726\nEpoch 262/1000\n - 4s - loss: 0.0648 - acc: 0.9759 - val_loss: 0.0692 - val_acc: 0.9731\nEpoch 263/1000\n - 4s - loss: 0.0653 - acc: 0.9754 - val_loss: 0.0691 - val_acc: 0.9731\nEpoch 264/1000\n - 4s - loss: 0.0651 - acc: 0.9760 - val_loss: 0.0693 - val_acc: 0.9726\nEpoch 265/1000\n - 4s - loss: 0.0640 - acc: 0.9763 - val_loss: 0.0691 - val_acc: 0.9731\nEpoch 266/1000\n - 4s - loss: 0.0640 - acc: 0.9770 - val_loss: 0.0689 - val_acc: 0.9731\nEpoch 267/1000\n - 4s - loss: 0.0635 - acc: 0.9756 - val_loss: 0.0690 - val_acc: 0.9726\nEpoch 268/1000\n - 4s - loss: 0.0637 - acc: 0.9764 - val_loss: 0.0688 - val_acc: 0.9731\nEpoch 269/1000\n - 4s - loss: 0.0644 - acc: 0.9768 - val_loss: 0.0688 - val_acc: 0.9726\nEpoch 270/1000\n - 4s - loss: 0.0637 - acc: 0.9769 - val_loss: 0.0691 - val_acc: 0.9731\nEpoch 271/1000\n - 4s - loss: 0.0639 - acc: 0.9771 - val_loss: 0.0688 - val_acc: 0.9726\nEpoch 272/1000\n - 4s - loss: 0.0622 - acc: 0.9771 - val_loss: 0.0688 - val_acc: 0.9726\nEpoch 273/1000\n - 4s - loss: 0.0635 - acc: 0.9768 - val_loss: 0.0688 - val_acc: 0.9731\nEpoch 274/1000\n - 4s - loss: 0.0635 - acc: 0.9763 - val_loss: 0.0687 - val_acc: 0.9726\nEpoch 275/1000\n - 4s - loss: 0.0632 - acc: 0.9771 - val_loss: 0.0685 - val_acc: 0.9726\nEpoch 276/1000\n - 4s - loss: 0.0643 - acc: 0.9760 - val_loss: 0.0684 - val_acc: 0.9726\nEpoch 277/1000\n - 4s - loss: 0.0631 - acc: 0.9770 - val_loss: 0.0686 - val_acc: 0.9737\nEpoch 278/1000\n - 4s - loss: 0.0627 - acc: 0.9764 - val_loss: 0.0683 - val_acc: 0.9726\nEpoch 279/1000\n - 4s - loss: 0.0634 - acc: 0.9771 - val_loss: 0.0685 - val_acc: 0.9726\nEpoch 280/1000\n - 4s - loss: 0.0631 - acc: 0.9766 - val_loss: 0.0683 - val_acc: 0.9737\nEpoch 281/1000\n - 4s - loss: 0.0626 - acc: 0.9771 - val_loss: 0.0683 - val_acc: 0.9726\nEpoch 282/1000\n - 4s - loss: 0.0628 - acc: 0.9768 - val_loss: 0.0683 - val_acc: 0.9726\nEpoch 283/1000\n - 4s - loss: 0.0628 - acc: 0.9771 - val_loss: 0.0682 - val_acc: 0.9726\nEpoch 284/1000\n - 4s - loss: 0.0621 - acc: 0.9768 - val_loss: 0.0684 - val_acc: 0.9737\nEpoch 285/1000\n - 4s - loss: 0.0621 - acc: 0.9768 - val_loss: 0.0682 - val_acc: 0.9731\nEpoch 286/1000\n - 4s - loss: 0.0623 - acc: 0.9761 - val_loss: 0.0680 - val_acc: 0.9726\nEpoch 287/1000\n - 4s - loss: 0.0617 - acc: 0.9778 - val_loss: 0.0681 - val_acc: 0.9726\nEpoch 288/1000\n - 4s - loss: 0.0627 - acc: 0.9772 - val_loss: 0.0679 - val_acc: 0.9731\nEpoch 289/1000\n - 4s - loss: 0.0629 - acc: 0.9773 - val_loss: 0.0681 - val_acc: 0.9731\nEpoch 290/1000\n - 4s - loss: 0.0620 - acc: 0.9774 - val_loss: 0.0679 - val_acc: 0.9726\nEpoch 291/1000\n - 4s - loss: 0.0613 - acc: 0.9775 - val_loss: 0.0679 - val_acc: 0.9731\nEpoch 292/1000\n - 4s - loss: 0.0627 - acc: 0.9769 - val_loss: 0.0677 - val_acc: 0.9726\nEpoch 293/1000\n - 4s - loss: 0.0623 - acc: 0.9770 - val_loss: 0.0677 - val_acc: 0.9726\nEpoch 294/1000\n - 4s - loss: 0.0613 - acc: 0.9773 - val_loss: 0.0677 - val_acc: 0.9731\nEpoch 295/1000\n - 4s - loss: 0.0617 - acc: 0.9771 - val_loss: 0.0679 - val_acc: 0.9737\nEpoch 296/1000\n - 4s - loss: 0.0621 - acc: 0.9773 - val_loss: 0.0675 - val_acc: 0.9731\nEpoch 297/1000\n - 4s - loss: 0.0621 - acc: 0.9772 - val_loss: 0.0676 - val_acc: 0.9731\nEpoch 298/1000\n - 4s - loss: 0.0607 - acc: 0.9777 - val_loss: 0.0675 - val_acc: 0.9726\nEpoch 299/1000\n - 4s - loss: 0.0620 - acc: 0.9764 - val_loss: 0.0676 - val_acc: 0.9731\nEpoch 300/1000\n - 4s - loss: 0.0608 - acc: 0.9780 - val_loss: 0.0674 - val_acc: 0.9731\nEpoch 301/1000\n - 4s - loss: 0.0614 - acc: 0.9768 - val_loss: 0.0676 - val_acc: 0.9737\nEpoch 302/1000\n - 4s - loss: 0.0620 - acc: 0.9772 - val_loss: 0.0672 - val_acc: 0.9731\nEpoch 303/1000\n - 4s - loss: 0.0613 - acc: 0.9776 - val_loss: 0.0675 - val_acc: 0.9743\nEpoch 304/1000\n - 4s - loss: 0.0596 - acc: 0.9791 - val_loss: 0.0671 - val_acc: 0.9737\nEpoch 305/1000\n - 4s - loss: 0.0608 - acc: 0.9777 - val_loss: 0.0673 - val_acc: 0.9731\nEpoch 306/1000\n - 4s - loss: 0.0598 - acc: 0.9779 - val_loss: 0.0673 - val_acc: 0.9743\nEpoch 307/1000\n - 4s - loss: 0.0604 - acc: 0.9782 - val_loss: 0.0671 - val_acc: 0.9737\nEpoch 308/1000\n - 4s - loss: 0.0603 - acc: 0.9785 - val_loss: 0.0671 - val_acc: 0.9737\nEpoch 309/1000\n - 4s - loss: 0.0610 - acc: 0.9784 - val_loss: 0.0670 - val_acc: 0.9749\nEpoch 310/1000\n - 4s - loss: 0.0603 - acc: 0.9786 - val_loss: 0.0668 - val_acc: 0.9731\nEpoch 311/1000\n - 4s - loss: 0.0605 - acc: 0.9777 - val_loss: 0.0671 - val_acc: 0.9743\nEpoch 312/1000\n - 4s - loss: 0.0605 - acc: 0.9785 - val_loss: 0.0670 - val_acc: 0.9749\nEpoch 313/1000\n - 4s - loss: 0.0598 - acc: 0.9783 - val_loss: 0.0670 - val_acc: 0.9743\nEpoch 314/1000\n - 4s - loss: 0.0602 - acc: 0.9772 - val_loss: 0.0668 - val_acc: 0.9737\nEpoch 315/1000\n - 4s - loss: 0.0597 - acc: 0.9782 - val_loss: 0.0669 - val_acc: 0.9749\nEpoch 316/1000\n - 4s - loss: 0.0602 - acc: 0.9790 - val_loss: 0.0667 - val_acc: 0.9743\nEpoch 317/1000\n - 4s - loss: 0.0595 - acc: 0.9796 - val_loss: 0.0668 - val_acc: 0.9743\nEpoch 318/1000\n - 4s - loss: 0.0599 - acc: 0.9776 - val_loss: 0.0669 - val_acc: 0.9743\nEpoch 319/1000\n - 4s - loss: 0.0599 - acc: 0.9783 - val_loss: 0.0669 - val_acc: 0.9743\nEpoch 320/1000\n - 4s - loss: 0.0596 - acc: 0.9780 - val_loss: 0.0666 - val_acc: 0.9743\nEpoch 321/1000\n - 4s - loss: 0.0587 - acc: 0.9781 - val_loss: 0.0667 - val_acc: 0.9743\nEpoch 322/1000\n - 4s - loss: 0.0598 - acc: 0.9779 - val_loss: 0.0664 - val_acc: 0.9743\nEpoch 323/1000\n - 4s - loss: 0.0598 - acc: 0.9792 - val_loss: 0.0665 - val_acc: 0.9743\nEpoch 324/1000\n - 4s - loss: 0.0588 - acc: 0.9780 - val_loss: 0.0664 - val_acc: 0.9743\nEpoch 325/1000\n - 4s - loss: 0.0595 - acc: 0.9782 - val_loss: 0.0664 - val_acc: 0.9743\nEpoch 326/1000\n - 4s - loss: 0.0588 - acc: 0.9783 - val_loss: 0.0663 - val_acc: 0.9743\nEpoch 327/1000\n - 4s - loss: 0.0600 - acc: 0.9771 - val_loss: 0.0663 - val_acc: 0.9743\nEpoch 328/1000\n - 4s - loss: 0.0591 - acc: 0.9783 - val_loss: 0.0662 - val_acc: 0.9737\nEpoch 329/1000\n - 4s - loss: 0.0592 - acc: 0.9777 - val_loss: 0.0663 - val_acc: 0.9749\nEpoch 330/1000\n - 4s - loss: 0.0588 - acc: 0.9787 - val_loss: 0.0663 - val_acc: 0.9749\nEpoch 331/1000\n - 4s - loss: 0.0582 - acc: 0.9802 - val_loss: 0.0663 - val_acc: 0.9743\nEpoch 332/1000\n - 4s - loss: 0.0579 - acc: 0.9788 - val_loss: 0.0662 - val_acc: 0.9731\nEpoch 333/1000\n - 4s - loss: 0.0589 - acc: 0.9784 - val_loss: 0.0662 - val_acc: 0.9731\nEpoch 334/1000\n - 4s - loss: 0.0593 - acc: 0.9789 - val_loss: 0.0662 - val_acc: 0.9743\nEpoch 335/1000\n - 4s - loss: 0.0582 - acc: 0.9793 - val_loss: 0.0661 - val_acc: 0.9754\nEpoch 336/1000\n - 4s - loss: 0.0579 - acc: 0.9785 - val_loss: 0.0660 - val_acc: 0.9743\nEpoch 337/1000\n - 4s - loss: 0.0586 - acc: 0.9788 - val_loss: 0.0662 - val_acc: 0.9737\nEpoch 338/1000\n - 4s - loss: 0.0582 - acc: 0.9787 - val_loss: 0.0660 - val_acc: 0.9743\nEpoch 339/1000\n - 4s - loss: 0.0580 - acc: 0.9797 - val_loss: 0.0659 - val_acc: 0.9743\nEpoch 340/1000\n - 4s - loss: 0.0580 - acc: 0.9787 - val_loss: 0.0660 - val_acc: 0.9749\nEpoch 341/1000\n - 4s - loss: 0.0582 - acc: 0.9782 - val_loss: 0.0660 - val_acc: 0.9737\nEpoch 342/1000\n - 4s - loss: 0.0581 - acc: 0.9786 - val_loss: 0.0662 - val_acc: 0.9737\nEpoch 343/1000\n - 4s - loss: 0.0579 - acc: 0.9790 - val_loss: 0.0658 - val_acc: 0.9737\nEpoch 344/1000\n - 4s - loss: 0.0565 - acc: 0.9792 - val_loss: 0.0661 - val_acc: 0.9737\nEpoch 345/1000\n - 4s - loss: 0.0571 - acc: 0.9786 - val_loss: 0.0659 - val_acc: 0.9749\nEpoch 346/1000\n - 4s - loss: 0.0578 - acc: 0.9784 - val_loss: 0.0658 - val_acc: 0.9749\nEpoch 347/1000\n - 4s - loss: 0.0580 - acc: 0.9784 - val_loss: 0.0656 - val_acc: 0.9737\nEpoch 348/1000\n - 4s - loss: 0.0565 - acc: 0.9790 - val_loss: 0.0655 - val_acc: 0.9737\nEpoch 349/1000\n - 4s - loss: 0.0573 - acc: 0.9796 - val_loss: 0.0655 - val_acc: 0.9737\nEpoch 350/1000\n - 4s - loss: 0.0573 - acc: 0.9796 - val_loss: 0.0656 - val_acc: 0.9737\nEpoch 351/1000\n - 4s - loss: 0.0571 - acc: 0.9785 - val_loss: 0.0655 - val_acc: 0.9731\nEpoch 352/1000\n - 4s - loss: 0.0569 - acc: 0.9795 - val_loss: 0.0654 - val_acc: 0.9743\nEpoch 353/1000\n - 4s - loss: 0.0571 - acc: 0.9793 - val_loss: 0.0654 - val_acc: 0.9743\nEpoch 354/1000\n - 4s - loss: 0.0566 - acc: 0.9797 - val_loss: 0.0653 - val_acc: 0.9737\nEpoch 355/1000\n - 4s - loss: 0.0562 - acc: 0.9787 - val_loss: 0.0653 - val_acc: 0.9737\nEpoch 356/1000\n - 4s - loss: 0.0566 - acc: 0.9795 - val_loss: 0.0652 - val_acc: 0.9737\nEpoch 357/1000\n - 4s - loss: 0.0572 - acc: 0.9800 - val_loss: 0.0654 - val_acc: 0.9749\nEpoch 358/1000\n - 4s - loss: 0.0564 - acc: 0.9797 - val_loss: 0.0652 - val_acc: 0.9743\nEpoch 359/1000\n - 4s - loss: 0.0569 - acc: 0.9795 - val_loss: 0.0651 - val_acc: 0.9737\nEpoch 360/1000\n - 4s - loss: 0.0564 - acc: 0.9794 - val_loss: 0.0653 - val_acc: 0.9743\nEpoch 361/1000\n - 4s - loss: 0.0560 - acc: 0.9808 - val_loss: 0.0651 - val_acc: 0.9743\nEpoch 362/1000\n - 4s - loss: 0.0563 - acc: 0.9799 - val_loss: 0.0651 - val_acc: 0.9749\nEpoch 363/1000\n - 4s - loss: 0.0564 - acc: 0.9790 - val_loss: 0.0653 - val_acc: 0.9737\nEpoch 364/1000\n - 4s - loss: 0.0564 - acc: 0.9786 - val_loss: 0.0650 - val_acc: 0.9743\nEpoch 365/1000\n - 4s - loss: 0.0566 - acc: 0.9800 - val_loss: 0.0650 - val_acc: 0.9743\nEpoch 366/1000\n - 4s - loss: 0.0558 - acc: 0.9806 - val_loss: 0.0650 - val_acc: 0.9743\nEpoch 367/1000\n - 4s - loss: 0.0565 - acc: 0.9799 - val_loss: 0.0650 - val_acc: 0.9743\nEpoch 368/1000\n - 4s - loss: 0.0555 - acc: 0.9796 - val_loss: 0.0650 - val_acc: 0.9743\nEpoch 369/1000\n - 4s - loss: 0.0550 - acc: 0.9798 - val_loss: 0.0650 - val_acc: 0.9737\nEpoch 370/1000\n - 4s - loss: 0.0557 - acc: 0.9804 - val_loss: 0.0649 - val_acc: 0.9743\nEpoch 371/1000\n - 4s - loss: 0.0558 - acc: 0.9799 - val_loss: 0.0649 - val_acc: 0.9743\nEpoch 372/1000\n - 4s - loss: 0.0553 - acc: 0.9796 - val_loss: 0.0648 - val_acc: 0.9743\nEpoch 373/1000\n - 4s - loss: 0.0566 - acc: 0.9795 - val_loss: 0.0651 - val_acc: 0.9743\nEpoch 374/1000\n - 4s - loss: 0.0555 - acc: 0.9798 - val_loss: 0.0647 - val_acc: 0.9743\nEpoch 375/1000\n - 4s - loss: 0.0556 - acc: 0.9802 - val_loss: 0.0648 - val_acc: 0.9743\nEpoch 376/1000\n - 4s - loss: 0.0555 - acc: 0.9792 - val_loss: 0.0648 - val_acc: 0.9749\nEpoch 377/1000\n - 4s - loss: 0.0550 - acc: 0.9793 - val_loss: 0.0648 - val_acc: 0.9743\nEpoch 378/1000\n - 4s - loss: 0.0558 - acc: 0.9792 - val_loss: 0.0648 - val_acc: 0.9737\nEpoch 379/1000\n - 4s - loss: 0.0554 - acc: 0.9799 - val_loss: 0.0650 - val_acc: 0.9743\nEpoch 380/1000\n - 4s - loss: 0.0548 - acc: 0.9807 - val_loss: 0.0647 - val_acc: 0.9743\nEpoch 381/1000\n - 4s - loss: 0.0558 - acc: 0.9796 - val_loss: 0.0646 - val_acc: 0.9737\nEpoch 382/1000\n - 4s - loss: 0.0546 - acc: 0.9810 - val_loss: 0.0647 - val_acc: 0.9743\nEpoch 383/1000\n - 4s - loss: 0.0552 - acc: 0.9798 - val_loss: 0.0646 - val_acc: 0.9737\nEpoch 384/1000\n - 4s - loss: 0.0549 - acc: 0.9801 - val_loss: 0.0646 - val_acc: 0.9749\nEpoch 385/1000\n - 4s - loss: 0.0548 - acc: 0.9804 - val_loss: 0.0645 - val_acc: 0.9749\nEpoch 386/1000\n - 4s - loss: 0.0542 - acc: 0.9799 - val_loss: 0.0644 - val_acc: 0.9743\nEpoch 387/1000\n - 4s - loss: 0.0549 - acc: 0.9801 - val_loss: 0.0645 - val_acc: 0.9743\nEpoch 388/1000\n - 4s - loss: 0.0543 - acc: 0.9803 - val_loss: 0.0647 - val_acc: 0.9749\nEpoch 389/1000\n - 4s - loss: 0.0553 - acc: 0.9792 - val_loss: 0.0648 - val_acc: 0.9749\nEpoch 390/1000\n - 4s - loss: 0.0556 - acc: 0.9790 - val_loss: 0.0644 - val_acc: 0.9749\nEpoch 391/1000\n - 4s - loss: 0.0543 - acc: 0.9798 - val_loss: 0.0642 - val_acc: 0.9743\nEpoch 392/1000\n - 4s - loss: 0.0539 - acc: 0.9804 - val_loss: 0.0645 - val_acc: 0.9749\nEpoch 393/1000\n - 4s - loss: 0.0541 - acc: 0.9805 - val_loss: 0.0642 - val_acc: 0.9743\nEpoch 394/1000\n - 4s - loss: 0.0539 - acc: 0.9801 - val_loss: 0.0642 - val_acc: 0.9743\nEpoch 395/1000\n - 4s - loss: 0.0545 - acc: 0.9803 - val_loss: 0.0642 - val_acc: 0.9743\nEpoch 396/1000\n - 4s - loss: 0.0532 - acc: 0.9813 - val_loss: 0.0641 - val_acc: 0.9743\nEpoch 397/1000\n - 4s - loss: 0.0539 - acc: 0.9802 - val_loss: 0.0644 - val_acc: 0.9743\nEpoch 398/1000\n - 4s - loss: 0.0538 - acc: 0.9808 - val_loss: 0.0643 - val_acc: 0.9749\nEpoch 399/1000\n - 4s - loss: 0.0534 - acc: 0.9813 - val_loss: 0.0644 - val_acc: 0.9749\nEpoch 400/1000\n - 4s - loss: 0.0548 - acc: 0.9799 - val_loss: 0.0641 - val_acc: 0.9737\nEpoch 401/1000\n - 4s - loss: 0.0545 - acc: 0.9798 - val_loss: 0.0641 - val_acc: 0.9743\nEpoch 402/1000\n - 4s - loss: 0.0540 - acc: 0.9807 - val_loss: 0.0641 - val_acc: 0.9737\nEpoch 403/1000\n - 4s - loss: 0.0537 - acc: 0.9808 - val_loss: 0.0641 - val_acc: 0.9749\nEpoch 404/1000\n - 4s - loss: 0.0535 - acc: 0.9810 - val_loss: 0.0640 - val_acc: 0.9743\nEpoch 405/1000\n - 4s - loss: 0.0534 - acc: 0.9801 - val_loss: 0.0639 - val_acc: 0.9743\nEpoch 406/1000\n - 4s - loss: 0.0535 - acc: 0.9800 - val_loss: 0.0640 - val_acc: 0.9749\nEpoch 407/1000\n - 4s - loss: 0.0527 - acc: 0.9808 - val_loss: 0.0640 - val_acc: 0.9749\nEpoch 408/1000\n - 4s - loss: 0.0533 - acc: 0.9802 - val_loss: 0.0640 - val_acc: 0.9743\nEpoch 409/1000\n - 4s - loss: 0.0542 - acc: 0.9801 - val_loss: 0.0639 - val_acc: 0.9749\nEpoch 410/1000\n - 4s - loss: 0.0532 - acc: 0.9807 - val_loss: 0.0638 - val_acc: 0.9737\nEpoch 411/1000\n - 4s - loss: 0.0524 - acc: 0.9810 - val_loss: 0.0638 - val_acc: 0.9749\nEpoch 412/1000\n - 4s - loss: 0.0521 - acc: 0.9806 - val_loss: 0.0639 - val_acc: 0.9749\nEpoch 413/1000\n - 4s - loss: 0.0530 - acc: 0.9813 - val_loss: 0.0637 - val_acc: 0.9737\nEpoch 414/1000\n - 4s - loss: 0.0525 - acc: 0.9806 - val_loss: 0.0640 - val_acc: 0.9749\nEpoch 415/1000\n - 4s - loss: 0.0528 - acc: 0.9806 - val_loss: 0.0638 - val_acc: 0.9743\nEpoch 416/1000\n - 4s - loss: 0.0533 - acc: 0.9809 - val_loss: 0.0639 - val_acc: 0.9743\nEpoch 417/1000\n - 4s - loss: 0.0534 - acc: 0.9804 - val_loss: 0.0637 - val_acc: 0.9749\nEpoch 418/1000\n - 4s - loss: 0.0525 - acc: 0.9806 - val_loss: 0.0635 - val_acc: 0.9743\nEpoch 419/1000\n - 4s - loss: 0.0530 - acc: 0.9811 - val_loss: 0.0638 - val_acc: 0.9754\nEpoch 420/1000\n - 4s - loss: 0.0522 - acc: 0.9814 - val_loss: 0.0636 - val_acc: 0.9749\nEpoch 421/1000\n - 4s - loss: 0.0527 - acc: 0.9805 - val_loss: 0.0636 - val_acc: 0.9743\nEpoch 422/1000\n - 4s - loss: 0.0522 - acc: 0.9811 - val_loss: 0.0635 - val_acc: 0.9749\nEpoch 423/1000\n - 4s - loss: 0.0533 - acc: 0.9807 - val_loss: 0.0635 - val_acc: 0.9749\nEpoch 424/1000\n - 4s - loss: 0.0520 - acc: 0.9823 - val_loss: 0.0634 - val_acc: 0.9749\nEpoch 425/1000\n - 4s - loss: 0.0517 - acc: 0.9812 - val_loss: 0.0634 - val_acc: 0.9749\nEpoch 426/1000\n - 4s - loss: 0.0518 - acc: 0.9814 - val_loss: 0.0635 - val_acc: 0.9749\nEpoch 427/1000\n - 4s - loss: 0.0521 - acc: 0.9811 - val_loss: 0.0635 - val_acc: 0.9749\nEpoch 428/1000\n - 4s - loss: 0.0526 - acc: 0.9811 - val_loss: 0.0636 - val_acc: 0.9749\nEpoch 429/1000\n - 4s - loss: 0.0519 - acc: 0.9813 - val_loss: 0.0635 - val_acc: 0.9749\nEpoch 430/1000\n - 4s - loss: 0.0517 - acc: 0.9813 - val_loss: 0.0633 - val_acc: 0.9749\nEpoch 431/1000\n - 4s - loss: 0.0521 - acc: 0.9806 - val_loss: 0.0632 - val_acc: 0.9743\nEpoch 432/1000\n - 4s - loss: 0.0523 - acc: 0.9814 - val_loss: 0.0631 - val_acc: 0.9754\nEpoch 433/1000\n - 4s - loss: 0.0523 - acc: 0.9813 - val_loss: 0.0632 - val_acc: 0.9749\nEpoch 434/1000\n - 4s - loss: 0.0512 - acc: 0.9817 - val_loss: 0.0633 - val_acc: 0.9754\nEpoch 435/1000\n - 4s - loss: 0.0517 - acc: 0.9808 - val_loss: 0.0636 - val_acc: 0.9754\nEpoch 436/1000\n - 4s - loss: 0.0523 - acc: 0.9817 - val_loss: 0.0632 - val_acc: 0.9754\nEpoch 437/1000\n - 4s - loss: 0.0514 - acc: 0.9820 - val_loss: 0.0631 - val_acc: 0.9749\nEpoch 438/1000\n - 4s - loss: 0.0519 - acc: 0.9814 - val_loss: 0.0630 - val_acc: 0.9754\nEpoch 439/1000\n - 4s - loss: 0.0514 - acc: 0.9815 - val_loss: 0.0629 - val_acc: 0.9754\nEpoch 440/1000\n - 4s - loss: 0.0519 - acc: 0.9817 - val_loss: 0.0631 - val_acc: 0.9754\nEpoch 441/1000\n - 4s - loss: 0.0519 - acc: 0.9804 - val_loss: 0.0629 - val_acc: 0.9754\nEpoch 442/1000\n - 4s - loss: 0.0513 - acc: 0.9811 - val_loss: 0.0630 - val_acc: 0.9749\nEpoch 443/1000\n - 4s - loss: 0.0510 - acc: 0.9809 - val_loss: 0.0629 - val_acc: 0.9754\nEpoch 444/1000\n - 4s - loss: 0.0513 - acc: 0.9815 - val_loss: 0.0629 - val_acc: 0.9760\nEpoch 445/1000\n - 4s - loss: 0.0517 - acc: 0.9813 - val_loss: 0.0630 - val_acc: 0.9749\nEpoch 446/1000\n - 4s - loss: 0.0512 - acc: 0.9813 - val_loss: 0.0630 - val_acc: 0.9743\nEpoch 447/1000\n - 4s - loss: 0.0511 - acc: 0.9830 - val_loss: 0.0628 - val_acc: 0.9754\nEpoch 448/1000\n - 4s - loss: 0.0512 - acc: 0.9816 - val_loss: 0.0628 - val_acc: 0.9754\nEpoch 449/1000\n - 4s - loss: 0.0502 - acc: 0.9817 - val_loss: 0.0629 - val_acc: 0.9760\nEpoch 450/1000\n - 4s - loss: 0.0509 - acc: 0.9814 - val_loss: 0.0627 - val_acc: 0.9754\nEpoch 451/1000\n - 4s - loss: 0.0514 - acc: 0.9804 - val_loss: 0.0627 - val_acc: 0.9754\nEpoch 452/1000\n - 4s - loss: 0.0511 - acc: 0.9817 - val_loss: 0.0630 - val_acc: 0.9754\nEpoch 453/1000\n - 4s - loss: 0.0511 - acc: 0.9817 - val_loss: 0.0631 - val_acc: 0.9754\nEpoch 454/1000\n - 4s - loss: 0.0505 - acc: 0.9819 - val_loss: 0.0627 - val_acc: 0.9760\nEpoch 455/1000\n - 4s - loss: 0.0506 - acc: 0.9814 - val_loss: 0.0626 - val_acc: 0.9760\nEpoch 456/1000\n - 4s - loss: 0.0503 - acc: 0.9828 - val_loss: 0.0626 - val_acc: 0.9760\nEpoch 457/1000\n - 4s - loss: 0.0504 - acc: 0.9820 - val_loss: 0.0627 - val_acc: 0.9754\nEpoch 458/1000\n - 4s - loss: 0.0512 - acc: 0.9815 - val_loss: 0.0628 - val_acc: 0.9754\nEpoch 459/1000\n - 4s - loss: 0.0510 - acc: 0.9808 - val_loss: 0.0626 - val_acc: 0.9754\nEpoch 460/1000\n - 4s - loss: 0.0500 - acc: 0.9820 - val_loss: 0.0626 - val_acc: 0.9754\nEpoch 461/1000\n - 4s - loss: 0.0509 - acc: 0.9820 - val_loss: 0.0625 - val_acc: 0.9754\nEpoch 462/1000\n - 4s - loss: 0.0499 - acc: 0.9827 - val_loss: 0.0625 - val_acc: 0.9760\nEpoch 463/1000\n - 4s - loss: 0.0499 - acc: 0.9823 - val_loss: 0.0623 - val_acc: 0.9760\nEpoch 464/1000\n - 4s - loss: 0.0501 - acc: 0.9816 - val_loss: 0.0624 - val_acc: 0.9754\nEpoch 465/1000\n - 4s - loss: 0.0502 - acc: 0.9809 - val_loss: 0.0624 - val_acc: 0.9754\nEpoch 466/1000\n - 4s - loss: 0.0498 - acc: 0.9816 - val_loss: 0.0626 - val_acc: 0.9754\nEpoch 467/1000\n - 4s - loss: 0.0502 - acc: 0.9817 - val_loss: 0.0626 - val_acc: 0.9754\nEpoch 468/1000\n - 4s - loss: 0.0504 - acc: 0.9820 - val_loss: 0.0623 - val_acc: 0.9754\nEpoch 469/1000\n - 4s - loss: 0.0495 - acc: 0.9819 - val_loss: 0.0628 - val_acc: 0.9760\nEpoch 470/1000\n - 4s - loss: 0.0506 - acc: 0.9818 - val_loss: 0.0623 - val_acc: 0.9760\nEpoch 471/1000\n - 4s - loss: 0.0503 - acc: 0.9822 - val_loss: 0.0622 - val_acc: 0.9749\nEpoch 472/1000\n - 4s - loss: 0.0496 - acc: 0.9821 - val_loss: 0.0622 - val_acc: 0.9760\nEpoch 473/1000\n - 4s - loss: 0.0498 - acc: 0.9827 - val_loss: 0.0623 - val_acc: 0.9754\nEpoch 474/1000\n - 4s - loss: 0.0481 - acc: 0.9821 - val_loss: 0.0623 - val_acc: 0.9760\nEpoch 475/1000\n - 4s - loss: 0.0490 - acc: 0.9813 - val_loss: 0.0624 - val_acc: 0.9754\nEpoch 476/1000\n - 4s - loss: 0.0492 - acc: 0.9822 - val_loss: 0.0624 - val_acc: 0.9749\nEpoch 477/1000\n - 4s - loss: 0.0488 - acc: 0.9822 - val_loss: 0.0623 - val_acc: 0.9754\nEpoch 478/1000\n - 4s - loss: 0.0489 - acc: 0.9827 - val_loss: 0.0623 - val_acc: 0.9760\nEpoch 479/1000\n - 4s - loss: 0.0490 - acc: 0.9821 - val_loss: 0.0623 - val_acc: 0.9760\nEpoch 480/1000\n - 4s - loss: 0.0496 - acc: 0.9815 - val_loss: 0.0621 - val_acc: 0.9754\nEpoch 481/1000\n - 4s - loss: 0.0490 - acc: 0.9819 - val_loss: 0.0621 - val_acc: 0.9766\nEpoch 482/1000\n - 4s - loss: 0.0495 - acc: 0.9818 - val_loss: 0.0621 - val_acc: 0.9754\nEpoch 483/1000\n - 4s - loss: 0.0483 - acc: 0.9827 - val_loss: 0.0620 - val_acc: 0.9760\nEpoch 484/1000\n - 4s - loss: 0.0494 - acc: 0.9812 - val_loss: 0.0620 - val_acc: 0.9760\nEpoch 485/1000\n - 4s - loss: 0.0488 - acc: 0.9816 - val_loss: 0.0620 - val_acc: 0.9760\nEpoch 486/1000\n - 4s - loss: 0.0493 - acc: 0.9818 - val_loss: 0.0620 - val_acc: 0.9760\nEpoch 487/1000\n - 4s - loss: 0.0485 - acc: 0.9821 - val_loss: 0.0620 - val_acc: 0.9760\nEpoch 488/1000\n - 4s - loss: 0.0485 - acc: 0.9827 - val_loss: 0.0618 - val_acc: 0.9760\nEpoch 489/1000\n - 4s - loss: 0.0491 - acc: 0.9824 - val_loss: 0.0618 - val_acc: 0.9760\nEpoch 490/1000\n - 4s - loss: 0.0486 - acc: 0.9825 - val_loss: 0.0620 - val_acc: 0.9766\nEpoch 491/1000\n - 4s - loss: 0.0479 - acc: 0.9827 - val_loss: 0.0619 - val_acc: 0.9760\nEpoch 492/1000\n - 4s - loss: 0.0473 - acc: 0.9830 - val_loss: 0.0619 - val_acc: 0.9766\nEpoch 493/1000\n - 4s - loss: 0.0480 - acc: 0.9828 - val_loss: 0.0617 - val_acc: 0.9754\nEpoch 494/1000\n - 4s - loss: 0.0484 - acc: 0.9829 - val_loss: 0.0617 - val_acc: 0.9760\nEpoch 495/1000\n - 4s - loss: 0.0483 - acc: 0.9829 - val_loss: 0.0617 - val_acc: 0.9766\nEpoch 496/1000\n - 4s - loss: 0.0483 - acc: 0.9833 - val_loss: 0.0617 - val_acc: 0.9754\nEpoch 497/1000\n - 4s - loss: 0.0485 - acc: 0.9825 - val_loss: 0.0617 - val_acc: 0.9760\nEpoch 498/1000\n - 4s - loss: 0.0486 - acc: 0.9823 - val_loss: 0.0619 - val_acc: 0.9760\nEpoch 499/1000\n - 4s - loss: 0.0479 - acc: 0.9829 - val_loss: 0.0616 - val_acc: 0.9760\nEpoch 500/1000\n - 4s - loss: 0.0482 - acc: 0.9818 - val_loss: 0.0616 - val_acc: 0.9760\nEpoch 501/1000\n - 4s - loss: 0.0482 - acc: 0.9820 - val_loss: 0.0614 - val_acc: 0.9754\nEpoch 502/1000\n - 4s - loss: 0.0486 - acc: 0.9821 - val_loss: 0.0617 - val_acc: 0.9760\nEpoch 503/1000\n - 4s - loss: 0.0481 - acc: 0.9817 - val_loss: 0.0614 - val_acc: 0.9760\nEpoch 504/1000\n - 4s - loss: 0.0487 - acc: 0.9823 - val_loss: 0.0613 - val_acc: 0.9760\nEpoch 505/1000\n - 4s - loss: 0.0483 - acc: 0.9828 - val_loss: 0.0615 - val_acc: 0.9766\nEpoch 506/1000\n - 4s - loss: 0.0474 - acc: 0.9831 - val_loss: 0.0614 - val_acc: 0.9766\nEpoch 507/1000\n - 4s - loss: 0.0480 - acc: 0.9829 - val_loss: 0.0615 - val_acc: 0.9760\nEpoch 508/1000\n - 4s - loss: 0.0474 - acc: 0.9829 - val_loss: 0.0614 - val_acc: 0.9766\nEpoch 509/1000\n - 4s - loss: 0.0476 - acc: 0.9830 - val_loss: 0.0615 - val_acc: 0.9766\nEpoch 510/1000\n - 4s - loss: 0.0483 - acc: 0.9827 - val_loss: 0.0612 - val_acc: 0.9754\nEpoch 511/1000\n - 4s - loss: 0.0473 - acc: 0.9829 - val_loss: 0.0618 - val_acc: 0.9754\nEpoch 512/1000\n - 4s - loss: 0.0468 - acc: 0.9837 - val_loss: 0.0614 - val_acc: 0.9754\nEpoch 513/1000\n - 4s - loss: 0.0470 - acc: 0.9835 - val_loss: 0.0613 - val_acc: 0.9754\nEpoch 514/1000\n - 4s - loss: 0.0470 - acc: 0.9823 - val_loss: 0.0614 - val_acc: 0.9766\nEpoch 515/1000\n - 4s - loss: 0.0471 - acc: 0.9824 - val_loss: 0.0612 - val_acc: 0.9749\nEpoch 516/1000\n - 4s - loss: 0.0470 - acc: 0.9827 - val_loss: 0.0614 - val_acc: 0.9760\nEpoch 517/1000\n - 4s - loss: 0.0466 - acc: 0.9820 - val_loss: 0.0617 - val_acc: 0.9760\nEpoch 518/1000\n - 4s - loss: 0.0466 - acc: 0.9836 - val_loss: 0.0611 - val_acc: 0.9760\nEpoch 519/1000\n - 4s - loss: 0.0471 - acc: 0.9836 - val_loss: 0.0611 - val_acc: 0.9760\nEpoch 520/1000\n - 4s - loss: 0.0482 - acc: 0.9825 - val_loss: 0.0611 - val_acc: 0.9766\nEpoch 521/1000\n - 4s - loss: 0.0464 - acc: 0.9830 - val_loss: 0.0611 - val_acc: 0.9749\nEpoch 522/1000\n - 4s - loss: 0.0466 - acc: 0.9828 - val_loss: 0.0611 - val_acc: 0.9760\nEpoch 523/1000\n - 4s - loss: 0.0473 - acc: 0.9829 - val_loss: 0.0611 - val_acc: 0.9760\nEpoch 524/1000\n - 4s - loss: 0.0470 - acc: 0.9828 - val_loss: 0.0610 - val_acc: 0.9749\nEpoch 525/1000\n - 4s - loss: 0.0472 - acc: 0.9827 - val_loss: 0.0612 - val_acc: 0.9754\nEpoch 526/1000\n - 4s - loss: 0.0471 - acc: 0.9829 - val_loss: 0.0612 - val_acc: 0.9754\nEpoch 527/1000\n - 4s - loss: 0.0466 - acc: 0.9839 - val_loss: 0.0610 - val_acc: 0.9754\nEpoch 528/1000\n - 4s - loss: 0.0457 - acc: 0.9844 - val_loss: 0.0612 - val_acc: 0.9754\nEpoch 529/1000\n - 4s - loss: 0.0471 - acc: 0.9834 - val_loss: 0.0609 - val_acc: 0.9760\nEpoch 530/1000\n - 4s - loss: 0.0472 - acc: 0.9839 - val_loss: 0.0608 - val_acc: 0.9760\nEpoch 531/1000\n - 4s - loss: 0.0467 - acc: 0.9823 - val_loss: 0.0608 - val_acc: 0.9766\nEpoch 532/1000\n - 4s - loss: 0.0473 - acc: 0.9828 - val_loss: 0.0610 - val_acc: 0.9749\nEpoch 533/1000\n - 4s - loss: 0.0449 - acc: 0.9837 - val_loss: 0.0609 - val_acc: 0.9749\nEpoch 534/1000\n - 4s - loss: 0.0467 - acc: 0.9827 - val_loss: 0.0608 - val_acc: 0.9754\nEpoch 535/1000\n - 4s - loss: 0.0459 - acc: 0.9836 - val_loss: 0.0608 - val_acc: 0.9754\nEpoch 536/1000\n - 4s - loss: 0.0464 - acc: 0.9829 - val_loss: 0.0608 - val_acc: 0.9766\nEpoch 537/1000\n - 4s - loss: 0.0461 - acc: 0.9831 - val_loss: 0.0608 - val_acc: 0.9754\nEpoch 538/1000\n - 4s - loss: 0.0456 - acc: 0.9843 - val_loss: 0.0608 - val_acc: 0.9760\nEpoch 539/1000\n - 4s - loss: 0.0472 - acc: 0.9830 - val_loss: 0.0608 - val_acc: 0.9749\nEpoch 540/1000\n - 4s - loss: 0.0457 - acc: 0.9837 - val_loss: 0.0609 - val_acc: 0.9754\nEpoch 541/1000\n - 4s - loss: 0.0452 - acc: 0.9846 - val_loss: 0.0608 - val_acc: 0.9754\nEpoch 542/1000\n - 4s - loss: 0.0457 - acc: 0.9833 - val_loss: 0.0608 - val_acc: 0.9760\nEpoch 543/1000\n - 4s - loss: 0.0466 - acc: 0.9838 - val_loss: 0.0607 - val_acc: 0.9754\nEpoch 544/1000\n - 4s - loss: 0.0457 - acc: 0.9834 - val_loss: 0.0608 - val_acc: 0.9754\nEpoch 545/1000\n - 4s - loss: 0.0460 - acc: 0.9834 - val_loss: 0.0605 - val_acc: 0.9766\nEpoch 546/1000\n - 4s - loss: 0.0460 - acc: 0.9841 - val_loss: 0.0607 - val_acc: 0.9760\nEpoch 547/1000\n - 4s - loss: 0.0454 - acc: 0.9834 - val_loss: 0.0607 - val_acc: 0.9760\nEpoch 548/1000\n - 4s - loss: 0.0462 - acc: 0.9834 - val_loss: 0.0607 - val_acc: 0.9760\nEpoch 549/1000\n - 4s - loss: 0.0455 - acc: 0.9839 - val_loss: 0.0606 - val_acc: 0.9754\nEpoch 550/1000\n - 4s - loss: 0.0463 - acc: 0.9834 - val_loss: 0.0606 - val_acc: 0.9754\nEpoch 551/1000\n - 4s - loss: 0.0460 - acc: 0.9839 - val_loss: 0.0606 - val_acc: 0.9760\nEpoch 552/1000\n - 4s - loss: 0.0453 - acc: 0.9836 - val_loss: 0.0605 - val_acc: 0.9760\nEpoch 553/1000\n - 4s - loss: 0.0451 - acc: 0.9837 - val_loss: 0.0603 - val_acc: 0.9766\nEpoch 554/1000\n - 4s - loss: 0.0451 - acc: 0.9835 - val_loss: 0.0605 - val_acc: 0.9754\nEpoch 555/1000\n - 4s - loss: 0.0453 - acc: 0.9832 - val_loss: 0.0606 - val_acc: 0.9760\nEpoch 556/1000\n - 4s - loss: 0.0445 - acc: 0.9827 - val_loss: 0.0607 - val_acc: 0.9754\nEpoch 557/1000\n - 4s - loss: 0.0444 - acc: 0.9834 - val_loss: 0.0607 - val_acc: 0.9760\nEpoch 558/1000\n - 4s - loss: 0.0456 - acc: 0.9843 - val_loss: 0.0606 - val_acc: 0.9760\nEpoch 559/1000\n - 4s - loss: 0.0450 - acc: 0.9839 - val_loss: 0.0603 - val_acc: 0.9760\nEpoch 560/1000\n - 4s - loss: 0.0454 - acc: 0.9836 - val_loss: 0.0605 - val_acc: 0.9754\nEpoch 561/1000\n - 4s - loss: 0.0448 - acc: 0.9844 - val_loss: 0.0602 - val_acc: 0.9760\nEpoch 562/1000\n - 4s - loss: 0.0453 - acc: 0.9832 - val_loss: 0.0603 - val_acc: 0.9760\nEpoch 563/1000\n - 4s - loss: 0.0454 - acc: 0.9833 - val_loss: 0.0602 - val_acc: 0.9754\nEpoch 564/1000\n - 4s - loss: 0.0449 - acc: 0.9830 - val_loss: 0.0601 - val_acc: 0.9749\nEpoch 565/1000\n - 4s - loss: 0.0449 - acc: 0.9842 - val_loss: 0.0604 - val_acc: 0.9760\nEpoch 566/1000\n - 4s - loss: 0.0446 - acc: 0.9842 - val_loss: 0.0603 - val_acc: 0.9749\nEpoch 567/1000\n - 4s - loss: 0.0458 - acc: 0.9836 - val_loss: 0.0604 - val_acc: 0.9754\nEpoch 568/1000\n - 4s - loss: 0.0446 - acc: 0.9841 - val_loss: 0.0606 - val_acc: 0.9760\nEpoch 569/1000\n - 4s - loss: 0.0441 - acc: 0.9843 - val_loss: 0.0604 - val_acc: 0.9754\nEpoch 570/1000\n - 4s - loss: 0.0447 - acc: 0.9836 - val_loss: 0.0604 - val_acc: 0.9754\nEpoch 571/1000\n - 4s - loss: 0.0439 - acc: 0.9844 - val_loss: 0.0604 - val_acc: 0.9749\nEpoch 572/1000\n - 4s - loss: 0.0460 - acc: 0.9836 - val_loss: 0.0604 - val_acc: 0.9754\nEpoch 573/1000\n - 4s - loss: 0.0440 - acc: 0.9838 - val_loss: 0.0603 - val_acc: 0.9749\nEpoch 574/1000\n - 4s - loss: 0.0445 - acc: 0.9834 - val_loss: 0.0605 - val_acc: 0.9754\nEpoch 575/1000\n - 4s - loss: 0.0446 - acc: 0.9835 - val_loss: 0.0603 - val_acc: 0.9754\nEpoch 576/1000\n - 4s - loss: 0.0447 - acc: 0.9837 - val_loss: 0.0602 - val_acc: 0.9754\nEpoch 577/1000\n - 4s - loss: 0.0437 - acc: 0.9837 - val_loss: 0.0601 - val_acc: 0.9754\nEpoch 578/1000\n - 4s - loss: 0.0441 - acc: 0.9846 - val_loss: 0.0600 - val_acc: 0.9754\nEpoch 579/1000\n - 4s - loss: 0.0447 - acc: 0.9841 - val_loss: 0.0601 - val_acc: 0.9754\nEpoch 580/1000\n - 4s - loss: 0.0440 - acc: 0.9841 - val_loss: 0.0602 - val_acc: 0.9749\nEpoch 581/1000\n - 4s - loss: 0.0439 - acc: 0.9842 - val_loss: 0.0601 - val_acc: 0.9749\nEpoch 582/1000\n - 4s - loss: 0.0440 - acc: 0.9843 - val_loss: 0.0600 - val_acc: 0.9754\nEpoch 583/1000\n - 4s - loss: 0.0439 - acc: 0.9842 - val_loss: 0.0600 - val_acc: 0.9760\nEpoch 584/1000\n - 4s - loss: 0.0446 - acc: 0.9835 - val_loss: 0.0600 - val_acc: 0.9749\nEpoch 585/1000\n - 4s - loss: 0.0443 - acc: 0.9839 - val_loss: 0.0600 - val_acc: 0.9754\nEpoch 586/1000\n - 4s - loss: 0.0446 - acc: 0.9834 - val_loss: 0.0600 - val_acc: 0.9749\nEpoch 587/1000\n - 4s - loss: 0.0439 - acc: 0.9841 - val_loss: 0.0600 - val_acc: 0.9754\nEpoch 588/1000\n - 4s - loss: 0.0439 - acc: 0.9844 - val_loss: 0.0601 - val_acc: 0.9749\nEpoch 589/1000\n - 4s - loss: 0.0438 - acc: 0.9845 - val_loss: 0.0600 - val_acc: 0.9760\nEpoch 590/1000\n - 4s - loss: 0.0435 - acc: 0.9843 - val_loss: 0.0605 - val_acc: 0.9766\nEpoch 591/1000\n - 4s - loss: 0.0444 - acc: 0.9836 - val_loss: 0.0599 - val_acc: 0.9749\nEpoch 592/1000\n - 4s - loss: 0.0438 - acc: 0.9839 - val_loss: 0.0600 - val_acc: 0.9754\nEpoch 593/1000\n - 4s - loss: 0.0437 - acc: 0.9846 - val_loss: 0.0599 - val_acc: 0.9749\nEpoch 594/1000\n - 4s - loss: 0.0435 - acc: 0.9849 - val_loss: 0.0598 - val_acc: 0.9760\nEpoch 595/1000\n - 4s - loss: 0.0432 - acc: 0.9850 - val_loss: 0.0599 - val_acc: 0.9749\nEpoch 596/1000\n - 4s - loss: 0.0428 - acc: 0.9846 - val_loss: 0.0598 - val_acc: 0.9743\nEpoch 597/1000\n - 4s - loss: 0.0434 - acc: 0.9841 - val_loss: 0.0597 - val_acc: 0.9749\nEpoch 598/1000\n - 4s - loss: 0.0435 - acc: 0.9846 - val_loss: 0.0597 - val_acc: 0.9749\nEpoch 599/1000\n - 4s - loss: 0.0435 - acc: 0.9842 - val_loss: 0.0598 - val_acc: 0.9749\nEpoch 600/1000\n - 4s - loss: 0.0443 - acc: 0.9846 - val_loss: 0.0598 - val_acc: 0.9743\nEpoch 601/1000\n - 4s - loss: 0.0426 - acc: 0.9855 - val_loss: 0.0598 - val_acc: 0.9743\nEpoch 602/1000\n - 4s - loss: 0.0427 - acc: 0.9842 - val_loss: 0.0597 - val_acc: 0.9749\nEpoch 603/1000\n - 4s - loss: 0.0433 - acc: 0.9844 - val_loss: 0.0598 - val_acc: 0.9749\nEpoch 604/1000\n - 4s - loss: 0.0436 - acc: 0.9846 - val_loss: 0.0598 - val_acc: 0.9743\nEpoch 605/1000\n - 4s - loss: 0.0434 - acc: 0.9843 - val_loss: 0.0597 - val_acc: 0.9749\nEpoch 606/1000\n - 4s - loss: 0.0441 - acc: 0.9839 - val_loss: 0.0598 - val_acc: 0.9749\nEpoch 607/1000\n - 4s - loss: 0.0431 - acc: 0.9843 - val_loss: 0.0598 - val_acc: 0.9749\nEpoch 608/1000\n - 4s - loss: 0.0431 - acc: 0.9844 - val_loss: 0.0596 - val_acc: 0.9749\nEpoch 609/1000\n - 4s - loss: 0.0420 - acc: 0.9849 - val_loss: 0.0596 - val_acc: 0.9749\nEpoch 610/1000\n - 4s - loss: 0.0425 - acc: 0.9854 - val_loss: 0.0600 - val_acc: 0.9760\nEpoch 611/1000\n - 4s - loss: 0.0429 - acc: 0.9844 - val_loss: 0.0595 - val_acc: 0.9737\nEpoch 612/1000\n - 4s - loss: 0.0439 - acc: 0.9837 - val_loss: 0.0596 - val_acc: 0.9743\nEpoch 613/1000\n - 4s - loss: 0.0421 - acc: 0.9851 - val_loss: 0.0597 - val_acc: 0.9749\nEpoch 614/1000\n - 4s - loss: 0.0429 - acc: 0.9843 - val_loss: 0.0597 - val_acc: 0.9749\nEpoch 615/1000\n - 4s - loss: 0.0432 - acc: 0.9848 - val_loss: 0.0596 - val_acc: 0.9749\nEpoch 616/1000\n - 4s - loss: 0.0422 - acc: 0.9851 - val_loss: 0.0597 - val_acc: 0.9737\nEpoch 617/1000\n - 4s - loss: 0.0433 - acc: 0.9846 - val_loss: 0.0597 - val_acc: 0.9749\nEpoch 618/1000\n - 4s - loss: 0.0425 - acc: 0.9838 - val_loss: 0.0594 - val_acc: 0.9754\nEpoch 619/1000\n - 4s - loss: 0.0417 - acc: 0.9850 - val_loss: 0.0595 - val_acc: 0.9754\nEpoch 620/1000\n - 4s - loss: 0.0431 - acc: 0.9841 - val_loss: 0.0594 - val_acc: 0.9754\nEpoch 621/1000\n - 4s - loss: 0.0418 - acc: 0.9858 - val_loss: 0.0595 - val_acc: 0.9754\nEpoch 622/1000\n - 4s - loss: 0.0424 - acc: 0.9839 - val_loss: 0.0594 - val_acc: 0.9743\nEpoch 623/1000\n - 4s - loss: 0.0428 - acc: 0.9848 - val_loss: 0.0594 - val_acc: 0.9737\nEpoch 624/1000\n - 4s - loss: 0.0424 - acc: 0.9848 - val_loss: 0.0595 - val_acc: 0.9749\nEpoch 625/1000\n - 4s - loss: 0.0419 - acc: 0.9847 - val_loss: 0.0595 - val_acc: 0.9743\nEpoch 626/1000\n - 4s - loss: 0.0417 - acc: 0.9861 - val_loss: 0.0594 - val_acc: 0.9749\nEpoch 627/1000\n - 4s - loss: 0.0420 - acc: 0.9846 - val_loss: 0.0596 - val_acc: 0.9749\nEpoch 628/1000\n - 4s - loss: 0.0421 - acc: 0.9851 - val_loss: 0.0594 - val_acc: 0.9743\nEpoch 629/1000\n - 4s - loss: 0.0417 - acc: 0.9850 - val_loss: 0.0595 - val_acc: 0.9743\nEpoch 630/1000\n - 4s - loss: 0.0428 - acc: 0.9846 - val_loss: 0.0600 - val_acc: 0.9766\nEpoch 631/1000\n - 4s - loss: 0.0423 - acc: 0.9849 - val_loss: 0.0593 - val_acc: 0.9743\nEpoch 632/1000\n - 4s - loss: 0.0418 - acc: 0.9844 - val_loss: 0.0594 - val_acc: 0.9754\nEpoch 633/1000\n - 4s - loss: 0.0421 - acc: 0.9852 - val_loss: 0.0593 - val_acc: 0.9743\nEpoch 634/1000\n - 4s - loss: 0.0410 - acc: 0.9847 - val_loss: 0.0593 - val_acc: 0.9743\nEpoch 635/1000\n - 4s - loss: 0.0420 - acc: 0.9848 - val_loss: 0.0593 - val_acc: 0.9737\nEpoch 636/1000\n - 4s - loss: 0.0422 - acc: 0.9846 - val_loss: 0.0594 - val_acc: 0.9749\nEpoch 637/1000\n - 4s - loss: 0.0415 - acc: 0.9853 - val_loss: 0.0593 - val_acc: 0.9737\nEpoch 638/1000\n - 4s - loss: 0.0420 - acc: 0.9851 - val_loss: 0.0593 - val_acc: 0.9749\nEpoch 639/1000\n - 4s - loss: 0.0421 - acc: 0.9848 - val_loss: 0.0593 - val_acc: 0.9743\nEpoch 640/1000\n - 4s - loss: 0.0421 - acc: 0.9845 - val_loss: 0.0593 - val_acc: 0.9737\nEpoch 641/1000\n - 4s - loss: 0.0413 - acc: 0.9853 - val_loss: 0.0594 - val_acc: 0.9754\nEpoch 642/1000\n - 4s - loss: 0.0420 - acc: 0.9853 - val_loss: 0.0593 - val_acc: 0.9754\nEpoch 643/1000\n - 4s - loss: 0.0409 - acc: 0.9859 - val_loss: 0.0594 - val_acc: 0.9766\nEpoch 644/1000\n - 4s - loss: 0.0412 - acc: 0.9854 - val_loss: 0.0592 - val_acc: 0.9754\nEpoch 645/1000\n - 4s - loss: 0.0418 - acc: 0.9846 - val_loss: 0.0592 - val_acc: 0.9743\nEpoch 646/1000\n - 4s - loss: 0.0416 - acc: 0.9855 - val_loss: 0.0592 - val_acc: 0.9743\nEpoch 647/1000\n - 4s - loss: 0.0408 - acc: 0.9857 - val_loss: 0.0592 - val_acc: 0.9749\nEpoch 648/1000\n - 4s - loss: 0.0409 - acc: 0.9851 - val_loss: 0.0590 - val_acc: 0.9743\nEpoch 649/1000\n - 4s - loss: 0.0411 - acc: 0.9853 - val_loss: 0.0594 - val_acc: 0.9754\nEpoch 650/1000\n - 4s - loss: 0.0428 - acc: 0.9843 - val_loss: 0.0591 - val_acc: 0.9749\nEpoch 651/1000\n - 4s - loss: 0.0414 - acc: 0.9850 - val_loss: 0.0592 - val_acc: 0.9754\nEpoch 652/1000\n - 4s - loss: 0.0405 - acc: 0.9856 - val_loss: 0.0591 - val_acc: 0.9766\nEpoch 653/1000\n - 4s - loss: 0.0407 - acc: 0.9850 - val_loss: 0.0590 - val_acc: 0.9743\nEpoch 654/1000\n - 4s - loss: 0.0413 - acc: 0.9849 - val_loss: 0.0592 - val_acc: 0.9754\nEpoch 655/1000\n - 4s - loss: 0.0410 - acc: 0.9844 - val_loss: 0.0592 - val_acc: 0.9749\nEpoch 656/1000\n - 4s - loss: 0.0411 - acc: 0.9851 - val_loss: 0.0590 - val_acc: 0.9749\nEpoch 657/1000\n - 4s - loss: 0.0408 - acc: 0.9858 - val_loss: 0.0590 - val_acc: 0.9749\nEpoch 658/1000\n - 4s - loss: 0.0416 - acc: 0.9845 - val_loss: 0.0590 - val_acc: 0.9743\nEpoch 659/1000\n - 4s - loss: 0.0408 - acc: 0.9860 - val_loss: 0.0589 - val_acc: 0.9743\nEpoch 660/1000\n - 4s - loss: 0.0409 - acc: 0.9862 - val_loss: 0.0589 - val_acc: 0.9749\nEpoch 661/1000\n - 4s - loss: 0.0406 - acc: 0.9853 - val_loss: 0.0589 - val_acc: 0.9749\nEpoch 662/1000\n - 4s - loss: 0.0406 - acc: 0.9859 - val_loss: 0.0590 - val_acc: 0.9743\nEpoch 663/1000\n - 4s - loss: 0.0407 - acc: 0.9858 - val_loss: 0.0591 - val_acc: 0.9760\nEpoch 664/1000\n - 4s - loss: 0.0407 - acc: 0.9848 - val_loss: 0.0589 - val_acc: 0.9743\nEpoch 665/1000\n - 4s - loss: 0.0399 - acc: 0.9853 - val_loss: 0.0591 - val_acc: 0.9737\nEpoch 666/1000\n - 4s - loss: 0.0405 - acc: 0.9848 - val_loss: 0.0588 - val_acc: 0.9743\nEpoch 667/1000\n - 4s - loss: 0.0405 - acc: 0.9855 - val_loss: 0.0588 - val_acc: 0.9749\nEpoch 668/1000\n - 4s - loss: 0.0407 - acc: 0.9849 - val_loss: 0.0588 - val_acc: 0.9743\nEpoch 669/1000\n - 4s - loss: 0.0385 - acc: 0.9873 - val_loss: 0.0588 - val_acc: 0.9749\nEpoch 670/1000\n - 4s - loss: 0.0395 - acc: 0.9852 - val_loss: 0.0588 - val_acc: 0.9743\nEpoch 671/1000\n - 4s - loss: 0.0407 - acc: 0.9850 - val_loss: 0.0586 - val_acc: 0.9743\nEpoch 672/1000\n - 4s - loss: 0.0399 - acc: 0.9853 - val_loss: 0.0586 - val_acc: 0.9743\nEpoch 673/1000\n - 4s - loss: 0.0413 - acc: 0.9849 - val_loss: 0.0587 - val_acc: 0.9737\nEpoch 674/1000\n - 4s - loss: 0.0404 - acc: 0.9858 - val_loss: 0.0587 - val_acc: 0.9743\nEpoch 675/1000\n - 4s - loss: 0.0399 - acc: 0.9861 - val_loss: 0.0587 - val_acc: 0.9749\nEpoch 676/1000\n - 4s - loss: 0.0397 - acc: 0.9860 - val_loss: 0.0588 - val_acc: 0.9743\nEpoch 677/1000\n - 4s - loss: 0.0402 - acc: 0.9858 - val_loss: 0.0588 - val_acc: 0.9743\nEpoch 678/1000\n - 4s - loss: 0.0398 - acc: 0.9853 - val_loss: 0.0588 - val_acc: 0.9743\nEpoch 679/1000\n - 4s - loss: 0.0402 - acc: 0.9846 - val_loss: 0.0587 - val_acc: 0.9743\nEpoch 680/1000\n - 4s - loss: 0.0402 - acc: 0.9853 - val_loss: 0.0586 - val_acc: 0.9749\nEpoch 681/1000\n - 4s - loss: 0.0399 - acc: 0.9860 - val_loss: 0.0586 - val_acc: 0.9743\nEpoch 682/1000\n - 4s - loss: 0.0396 - acc: 0.9867 - val_loss: 0.0586 - val_acc: 0.9749\nEpoch 683/1000\n - 4s - loss: 0.0406 - acc: 0.9856 - val_loss: 0.0586 - val_acc: 0.9754\nEpoch 684/1000\n - 4s - loss: 0.0402 - acc: 0.9856 - val_loss: 0.0585 - val_acc: 0.9749\nEpoch 685/1000\n - 4s - loss: 0.0395 - acc: 0.9867 - val_loss: 0.0585 - val_acc: 0.9743\nEpoch 686/1000\n - 4s - loss: 0.0395 - acc: 0.9853 - val_loss: 0.0586 - val_acc: 0.9754\nEpoch 687/1000\n - 4s - loss: 0.0398 - acc: 0.9851 - val_loss: 0.0587 - val_acc: 0.9760\nEpoch 688/1000\n - 4s - loss: 0.0396 - acc: 0.9857 - val_loss: 0.0587 - val_acc: 0.9743\nEpoch 689/1000\n - 4s - loss: 0.0393 - acc: 0.9863 - val_loss: 0.0585 - val_acc: 0.9743\nEpoch 690/1000\n - 4s - loss: 0.0392 - acc: 0.9855 - val_loss: 0.0587 - val_acc: 0.9749\nEpoch 691/1000\n - 4s - loss: 0.0392 - acc: 0.9861 - val_loss: 0.0586 - val_acc: 0.9743\nEpoch 692/1000\n - 4s - loss: 0.0400 - acc: 0.9857 - val_loss: 0.0586 - val_acc: 0.9743\nEpoch 693/1000\n - 4s - loss: 0.0397 - acc: 0.9855 - val_loss: 0.0584 - val_acc: 0.9749\nEpoch 694/1000\n - 4s - loss: 0.0403 - acc: 0.9857 - val_loss: 0.0584 - val_acc: 0.9749\nEpoch 695/1000\n - 4s - loss: 0.0392 - acc: 0.9874 - val_loss: 0.0584 - val_acc: 0.9754\nEpoch 696/1000\n - 4s - loss: 0.0390 - acc: 0.9868 - val_loss: 0.0585 - val_acc: 0.9743\nEpoch 697/1000\n - 4s - loss: 0.0396 - acc: 0.9858 - val_loss: 0.0590 - val_acc: 0.9771\nEpoch 698/1000\n - 4s - loss: 0.0387 - acc: 0.9862 - val_loss: 0.0587 - val_acc: 0.9760\nEpoch 699/1000\n - 4s - loss: 0.0386 - acc: 0.9855 - val_loss: 0.0588 - val_acc: 0.9766\nEpoch 700/1000\n - 4s - loss: 0.0392 - acc: 0.9854 - val_loss: 0.0585 - val_acc: 0.9754\nEpoch 701/1000\n - 4s - loss: 0.0391 - acc: 0.9860 - val_loss: 0.0585 - val_acc: 0.9754\nEpoch 702/1000\n - 4s - loss: 0.0389 - acc: 0.9860 - val_loss: 0.0584 - val_acc: 0.9743\nEpoch 703/1000\n - 4s - loss: 0.0390 - acc: 0.9863 - val_loss: 0.0583 - val_acc: 0.9766\nEpoch 704/1000\n - 4s - loss: 0.0389 - acc: 0.9857 - val_loss: 0.0585 - val_acc: 0.9766\nEpoch 705/1000\n - 4s - loss: 0.0396 - acc: 0.9855 - val_loss: 0.0586 - val_acc: 0.9766\nEpoch 706/1000\n - 4s - loss: 0.0395 - acc: 0.9863 - val_loss: 0.0589 - val_acc: 0.9766\nEpoch 707/1000\n - 4s - loss: 0.0391 - acc: 0.9869 - val_loss: 0.0584 - val_acc: 0.9749\nEpoch 708/1000\n - 4s - loss: 0.0395 - acc: 0.9853 - val_loss: 0.0586 - val_acc: 0.9760\nEpoch 709/1000\n - 4s - loss: 0.0396 - acc: 0.9862 - val_loss: 0.0582 - val_acc: 0.9754\nEpoch 710/1000\n - 4s - loss: 0.0390 - acc: 0.9857 - val_loss: 0.0582 - val_acc: 0.9749\nEpoch 711/1000\n - 4s - loss: 0.0386 - acc: 0.9865 - val_loss: 0.0583 - val_acc: 0.9766\nEpoch 712/1000\n - 4s - loss: 0.0395 - acc: 0.9858 - val_loss: 0.0582 - val_acc: 0.9749\nEpoch 713/1000\n - 4s - loss: 0.0389 - acc: 0.9854 - val_loss: 0.0585 - val_acc: 0.9743\nEpoch 714/1000\n - 4s - loss: 0.0388 - acc: 0.9865 - val_loss: 0.0582 - val_acc: 0.9743\nEpoch 715/1000\n - 4s - loss: 0.0393 - acc: 0.9851 - val_loss: 0.0584 - val_acc: 0.9749\nEpoch 716/1000\n - 4s - loss: 0.0382 - acc: 0.9862 - val_loss: 0.0585 - val_acc: 0.9743\nEpoch 717/1000\n - 4s - loss: 0.0382 - acc: 0.9868 - val_loss: 0.0585 - val_acc: 0.9760\nEpoch 718/1000\n - 4s - loss: 0.0389 - acc: 0.9856 - val_loss: 0.0584 - val_acc: 0.9749\nEpoch 719/1000\n - 4s - loss: 0.0383 - acc: 0.9859 - val_loss: 0.0584 - val_acc: 0.9766\nEpoch 720/1000\n - 4s - loss: 0.0382 - acc: 0.9869 - val_loss: 0.0580 - val_acc: 0.9749\nEpoch 721/1000\n - 4s - loss: 0.0385 - acc: 0.9868 - val_loss: 0.0582 - val_acc: 0.9760\nEpoch 722/1000\n - 4s - loss: 0.0380 - acc: 0.9870 - val_loss: 0.0581 - val_acc: 0.9743\nEpoch 723/1000\n - 4s - loss: 0.0387 - acc: 0.9866 - val_loss: 0.0581 - val_acc: 0.9749\nEpoch 724/1000\n - 4s - loss: 0.0382 - acc: 0.9861 - val_loss: 0.0583 - val_acc: 0.9777\nEpoch 725/1000\n - 4s - loss: 0.0377 - acc: 0.9868 - val_loss: 0.0584 - val_acc: 0.9760\nEpoch 726/1000\n - 4s - loss: 0.0387 - acc: 0.9860 - val_loss: 0.0581 - val_acc: 0.9760\nEpoch 727/1000\n - 4s - loss: 0.0376 - acc: 0.9858 - val_loss: 0.0582 - val_acc: 0.9749\nEpoch 728/1000\n - 4s - loss: 0.0384 - acc: 0.9860 - val_loss: 0.0582 - val_acc: 0.9749\nEpoch 729/1000\n - 4s - loss: 0.0377 - acc: 0.9859 - val_loss: 0.0581 - val_acc: 0.9743\nEpoch 730/1000\n - 4s - loss: 0.0383 - acc: 0.9867 - val_loss: 0.0581 - val_acc: 0.9743\nEpoch 731/1000\n - 4s - loss: 0.0387 - acc: 0.9854 - val_loss: 0.0581 - val_acc: 0.9760\nEpoch 732/1000\n - 4s - loss: 0.0379 - acc: 0.9870 - val_loss: 0.0583 - val_acc: 0.9760\nEpoch 733/1000\n - 4s - loss: 0.0379 - acc: 0.9863 - val_loss: 0.0582 - val_acc: 0.9766\nEpoch 734/1000\n - 4s - loss: 0.0379 - acc: 0.9867 - val_loss: 0.0580 - val_acc: 0.9754\nEpoch 735/1000\n - 4s - loss: 0.0398 - acc: 0.9856 - val_loss: 0.0579 - val_acc: 0.9754\nEpoch 736/1000\n - 4s - loss: 0.0376 - acc: 0.9869 - val_loss: 0.0580 - val_acc: 0.9754\nEpoch 737/1000\n - 4s - loss: 0.0375 - acc: 0.9867 - val_loss: 0.0579 - val_acc: 0.9754\nEpoch 738/1000\n - 4s - loss: 0.0377 - acc: 0.9865 - val_loss: 0.0581 - val_acc: 0.9760\nEpoch 739/1000\n - 4s - loss: 0.0384 - acc: 0.9867 - val_loss: 0.0580 - val_acc: 0.9749\nEpoch 740/1000\n - 4s - loss: 0.0364 - acc: 0.9876 - val_loss: 0.0584 - val_acc: 0.9766\nEpoch 741/1000\n - 4s - loss: 0.0380 - acc: 0.9857 - val_loss: 0.0581 - val_acc: 0.9766\nEpoch 742/1000\n - 4s - loss: 0.0378 - acc: 0.9865 - val_loss: 0.0578 - val_acc: 0.9760\nEpoch 743/1000\n - 4s - loss: 0.0371 - acc: 0.9870 - val_loss: 0.0578 - val_acc: 0.9754\nEpoch 744/1000\n - 4s - loss: 0.0378 - acc: 0.9870 - val_loss: 0.0576 - val_acc: 0.9760\nEpoch 745/1000\n - 4s - loss: 0.0388 - acc: 0.9866 - val_loss: 0.0575 - val_acc: 0.9754\nEpoch 746/1000\n - 4s - loss: 0.0376 - acc: 0.9869 - val_loss: 0.0584 - val_acc: 0.9766\nEpoch 747/1000\n - 4s - loss: 0.0374 - acc: 0.9871 - val_loss: 0.0583 - val_acc: 0.9749\nEpoch 748/1000\n - 4s - loss: 0.0376 - acc: 0.9863 - val_loss: 0.0579 - val_acc: 0.9743\nEpoch 749/1000\n - 4s - loss: 0.0371 - acc: 0.9871 - val_loss: 0.0579 - val_acc: 0.9749\nEpoch 750/1000\n - 4s - loss: 0.0378 - acc: 0.9865 - val_loss: 0.0578 - val_acc: 0.9749\nEpoch 751/1000\n - 4s - loss: 0.0375 - acc: 0.9872 - val_loss: 0.0580 - val_acc: 0.9743\nEpoch 752/1000\n - 4s - loss: 0.0375 - acc: 0.9862 - val_loss: 0.0579 - val_acc: 0.9749\nEpoch 753/1000\n - 4s - loss: 0.0369 - acc: 0.9865 - val_loss: 0.0579 - val_acc: 0.9754\nEpoch 754/1000\n - 4s - loss: 0.0375 - acc: 0.9867 - val_loss: 0.0577 - val_acc: 0.9754\nEpoch 755/1000\n - 4s - loss: 0.0374 - acc: 0.9872 - val_loss: 0.0576 - val_acc: 0.9749\nEpoch 756/1000\n - 4s - loss: 0.0377 - acc: 0.9865 - val_loss: 0.0577 - val_acc: 0.9766\nEpoch 757/1000\n - 4s - loss: 0.0374 - acc: 0.9870 - val_loss: 0.0579 - val_acc: 0.9743\nEpoch 758/1000\n - 4s - loss: 0.0377 - acc: 0.9870 - val_loss: 0.0580 - val_acc: 0.9749\nEpoch 759/1000\n - 4s - loss: 0.0368 - acc: 0.9872 - val_loss: 0.0580 - val_acc: 0.9749\nEpoch 760/1000\n - 4s - loss: 0.0373 - acc: 0.9863 - val_loss: 0.0578 - val_acc: 0.9771\nEpoch 761/1000\n - 4s - loss: 0.0371 - acc: 0.9872 - val_loss: 0.0581 - val_acc: 0.9760\nEpoch 762/1000\n - 4s - loss: 0.0369 - acc: 0.9867 - val_loss: 0.0578 - val_acc: 0.9766\nEpoch 763/1000\n - 4s - loss: 0.0369 - acc: 0.9864 - val_loss: 0.0577 - val_acc: 0.9754\nEpoch 764/1000\n - 4s - loss: 0.0374 - acc: 0.9867 - val_loss: 0.0581 - val_acc: 0.9760\nEpoch 765/1000\n - 4s - loss: 0.0360 - acc: 0.9869 - val_loss: 0.0578 - val_acc: 0.9766\nEpoch 766/1000\n - 4s - loss: 0.0362 - acc: 0.9876 - val_loss: 0.0575 - val_acc: 0.9760\nEpoch 767/1000\n - 4s - loss: 0.0364 - acc: 0.9875 - val_loss: 0.0576 - val_acc: 0.9760\nEpoch 768/1000\n - 4s - loss: 0.0367 - acc: 0.9873 - val_loss: 0.0577 - val_acc: 0.9760\nEpoch 769/1000\n - 4s - loss: 0.0364 - acc: 0.9876 - val_loss: 0.0578 - val_acc: 0.9766\nEpoch 770/1000\n - 4s - loss: 0.0372 - acc: 0.9867 - val_loss: 0.0575 - val_acc: 0.9754\nEpoch 771/1000\n - 4s - loss: 0.0360 - acc: 0.9870 - val_loss: 0.0578 - val_acc: 0.9749\nEpoch 772/1000\n - 4s - loss: 0.0364 - acc: 0.9879 - val_loss: 0.0579 - val_acc: 0.9749\nEpoch 773/1000\n - 4s - loss: 0.0371 - acc: 0.9866 - val_loss: 0.0580 - val_acc: 0.9760\nEpoch 774/1000\n - 4s - loss: 0.0371 - acc: 0.9863 - val_loss: 0.0576 - val_acc: 0.9760\nEpoch 775/1000\n - 4s - loss: 0.0363 - acc: 0.9874 - val_loss: 0.0578 - val_acc: 0.9754\nEpoch 776/1000\n - 4s - loss: 0.0368 - acc: 0.9875 - val_loss: 0.0579 - val_acc: 0.9760\nEpoch 777/1000\n - 4s - loss: 0.0370 - acc: 0.9867 - val_loss: 0.0577 - val_acc: 0.9760\nEpoch 778/1000\n - 4s - loss: 0.0353 - acc: 0.9879 - val_loss: 0.0578 - val_acc: 0.9749\nEpoch 779/1000\n - 4s - loss: 0.0362 - acc: 0.9878 - val_loss: 0.0577 - val_acc: 0.9760\nEpoch 780/1000\n - 4s - loss: 0.0366 - acc: 0.9870 - val_loss: 0.0576 - val_acc: 0.9760\nEpoch 781/1000\n - 4s - loss: 0.0353 - acc: 0.9884 - val_loss: 0.0575 - val_acc: 0.9760\nEpoch 782/1000\n - 4s - loss: 0.0360 - acc: 0.9881 - val_loss: 0.0576 - val_acc: 0.9760\nEpoch 783/1000\n - 4s - loss: 0.0369 - acc: 0.9870 - val_loss: 0.0577 - val_acc: 0.9766\nEpoch 784/1000\n - 4s - loss: 0.0359 - acc: 0.9868 - val_loss: 0.0578 - val_acc: 0.9754\nEpoch 785/1000\n - 4s - loss: 0.0363 - acc: 0.9872 - val_loss: 0.0575 - val_acc: 0.9766\nEpoch 786/1000\n - 4s - loss: 0.0356 - acc: 0.9883 - val_loss: 0.0576 - val_acc: 0.9749\nEpoch 787/1000\n - 4s - loss: 0.0367 - acc: 0.9876 - val_loss: 0.0575 - val_acc: 0.9754\nEpoch 788/1000\n - 4s - loss: 0.0356 - acc: 0.9875 - val_loss: 0.0577 - val_acc: 0.9749\nEpoch 789/1000\n - 4s - loss: 0.0362 - acc: 0.9873 - val_loss: 0.0577 - val_acc: 0.9760\nEpoch 790/1000\n - 4s - loss: 0.0363 - acc: 0.9870 - val_loss: 0.0576 - val_acc: 0.9766\nEpoch 791/1000\n - 4s - loss: 0.0365 - acc: 0.9874 - val_loss: 0.0577 - val_acc: 0.9754\nEpoch 792/1000\n - 4s - loss: 0.0355 - acc: 0.9877 - val_loss: 0.0579 - val_acc: 0.9766\nEpoch 793/1000\n - 4s - loss: 0.0353 - acc: 0.9878 - val_loss: 0.0576 - val_acc: 0.9760\nEpoch 794/1000\n - 4s - loss: 0.0365 - acc: 0.9866 - val_loss: 0.0576 - val_acc: 0.9760\nEpoch 795/1000\n - 4s - loss: 0.0352 - acc: 0.9888 - val_loss: 0.0578 - val_acc: 0.9760\nEpoch 796/1000\n - 4s - loss: 0.0364 - acc: 0.9875 - val_loss: 0.0577 - val_acc: 0.9760\nEpoch 797/1000\n - 4s - loss: 0.0361 - acc: 0.9876 - val_loss: 0.0577 - val_acc: 0.9760\nEpoch 798/1000\n - 4s - loss: 0.0351 - acc: 0.9875 - val_loss: 0.0576 - val_acc: 0.9760\nEpoch 799/1000\n - 4s - loss: 0.0359 - acc: 0.9863 - val_loss: 0.0576 - val_acc: 0.9760\nEpoch 800/1000\n - 4s - loss: 0.0364 - acc: 0.9868 - val_loss: 0.0574 - val_acc: 0.9760\nEpoch 801/1000\n - 4s - loss: 0.0353 - acc: 0.9879 - val_loss: 0.0575 - val_acc: 0.9754\nEpoch 802/1000\n - 4s - loss: 0.0366 - acc: 0.9870 - val_loss: 0.0573 - val_acc: 0.9760\nEpoch 803/1000\n - 4s - loss: 0.0360 - acc: 0.9879 - val_loss: 0.0574 - val_acc: 0.9771\nEpoch 804/1000\n - 4s - loss: 0.0352 - acc: 0.9876 - val_loss: 0.0574 - val_acc: 0.9766\nEpoch 805/1000\n - 4s - loss: 0.0356 - acc: 0.9879 - val_loss: 0.0573 - val_acc: 0.9760\nEpoch 806/1000\n - 4s - loss: 0.0361 - acc: 0.9872 - val_loss: 0.0578 - val_acc: 0.9760\nEpoch 807/1000\n - 4s - loss: 0.0349 - acc: 0.9884 - val_loss: 0.0576 - val_acc: 0.9749\nEpoch 808/1000\n - 4s - loss: 0.0357 - acc: 0.9880 - val_loss: 0.0579 - val_acc: 0.9760\nEpoch 809/1000\n - 4s - loss: 0.0353 - acc: 0.9876 - val_loss: 0.0575 - val_acc: 0.9766\nEpoch 810/1000\n - 4s - loss: 0.0353 - acc: 0.9884 - val_loss: 0.0576 - val_acc: 0.9766\nEpoch 811/1000\n - 4s - loss: 0.0360 - acc: 0.9880 - val_loss: 0.0575 - val_acc: 0.9760\nEpoch 812/1000\n - 4s - loss: 0.0344 - acc: 0.9888 - val_loss: 0.0575 - val_acc: 0.9766\nEpoch 813/1000\n - 4s - loss: 0.0358 - acc: 0.9871 - val_loss: 0.0576 - val_acc: 0.9760\nEpoch 814/1000\n - 4s - loss: 0.0354 - acc: 0.9872 - val_loss: 0.0575 - val_acc: 0.9760\nEpoch 815/1000\n - 4s - loss: 0.0362 - acc: 0.9874 - val_loss: 0.0570 - val_acc: 0.9771\nEpoch 816/1000\n - 4s - loss: 0.0346 - acc: 0.9883 - val_loss: 0.0571 - val_acc: 0.9771\nEpoch 817/1000\n - 4s - loss: 0.0354 - acc: 0.9872 - val_loss: 0.0572 - val_acc: 0.9771\nEpoch 818/1000\n - 4s - loss: 0.0352 - acc: 0.9875 - val_loss: 0.0574 - val_acc: 0.9760\nEpoch 819/1000\n - 4s - loss: 0.0347 - acc: 0.9880 - val_loss: 0.0572 - val_acc: 0.9766\nEpoch 820/1000\n - 4s - loss: 0.0349 - acc: 0.9879 - val_loss: 0.0573 - val_acc: 0.9766\nEpoch 821/1000\n - 4s - loss: 0.0337 - acc: 0.9888 - val_loss: 0.0573 - val_acc: 0.9760\nEpoch 822/1000\n - 4s - loss: 0.0340 - acc: 0.9883 - val_loss: 0.0574 - val_acc: 0.9771\nEpoch 823/1000\n - 4s - loss: 0.0348 - acc: 0.9884 - val_loss: 0.0573 - val_acc: 0.9766\nEpoch 824/1000\n - 4s - loss: 0.0353 - acc: 0.9877 - val_loss: 0.0575 - val_acc: 0.9766\nEpoch 825/1000\n - 4s - loss: 0.0345 - acc: 0.9877 - val_loss: 0.0575 - val_acc: 0.9766\nEpoch 826/1000\n - 4s - loss: 0.0357 - acc: 0.9872 - val_loss: 0.0575 - val_acc: 0.9760\nEpoch 827/1000\n - 4s - loss: 0.0348 - acc: 0.9878 - val_loss: 0.0573 - val_acc: 0.9766\nEpoch 828/1000\n - 4s - loss: 0.0349 - acc: 0.9876 - val_loss: 0.0576 - val_acc: 0.9766\nEpoch 829/1000\n - 4s - loss: 0.0343 - acc: 0.9879 - val_loss: 0.0573 - val_acc: 0.9760\nEpoch 830/1000\n - 4s - loss: 0.0353 - acc: 0.9881 - val_loss: 0.0572 - val_acc: 0.9777\nEpoch 831/1000\n - 4s - loss: 0.0352 - acc: 0.9878 - val_loss: 0.0571 - val_acc: 0.9766\nEpoch 832/1000\n - 4s - loss: 0.0353 - acc: 0.9880 - val_loss: 0.0573 - val_acc: 0.9766\nEpoch 833/1000\n - 4s - loss: 0.0344 - acc: 0.9882 - val_loss: 0.0574 - val_acc: 0.9766\nEpoch 834/1000\n - 4s - loss: 0.0344 - acc: 0.9878 - val_loss: 0.0576 - val_acc: 0.9760\nEpoch 835/1000\n - 4s - loss: 0.0350 - acc: 0.9876 - val_loss: 0.0572 - val_acc: 0.9766\nEpoch 836/1000\n - 4s - loss: 0.0338 - acc: 0.9884 - val_loss: 0.0574 - val_acc: 0.9766\nEpoch 837/1000\n - 4s - loss: 0.0347 - acc: 0.9877 - val_loss: 0.0573 - val_acc: 0.9760\nEpoch 838/1000\n - 4s - loss: 0.0344 - acc: 0.9882 - val_loss: 0.0573 - val_acc: 0.9766\nEpoch 839/1000\n - 4s - loss: 0.0338 - acc: 0.9883 - val_loss: 0.0571 - val_acc: 0.9766\nEpoch 840/1000\n - 4s - loss: 0.0333 - acc: 0.9884 - val_loss: 0.0571 - val_acc: 0.9766\nEpoch 841/1000\n - 4s - loss: 0.0345 - acc: 0.9881 - val_loss: 0.0574 - val_acc: 0.9766\nEpoch 842/1000\n - 4s - loss: 0.0341 - acc: 0.9883 - val_loss: 0.0572 - val_acc: 0.9777\nEpoch 843/1000\n - 4s - loss: 0.0353 - acc: 0.9869 - val_loss: 0.0573 - val_acc: 0.9766\nEpoch 844/1000\n - 4s - loss: 0.0331 - acc: 0.9884 - val_loss: 0.0572 - val_acc: 0.9766\nEpoch 845/1000\n - 4s - loss: 0.0352 - acc: 0.9881 - val_loss: 0.0571 - val_acc: 0.9766\nEpoch 846/1000\n - 4s - loss: 0.0340 - acc: 0.9883 - val_loss: 0.0572 - val_acc: 0.9771\nEpoch 847/1000\n - 4s - loss: 0.0343 - acc: 0.9883 - val_loss: 0.0571 - val_acc: 0.9766\nEpoch 848/1000\n - 4s - loss: 0.0341 - acc: 0.9881 - val_loss: 0.0571 - val_acc: 0.9766\nEpoch 849/1000\n - 4s - loss: 0.0338 - acc: 0.9882 - val_loss: 0.0574 - val_acc: 0.9760\nEpoch 850/1000\n - 4s - loss: 0.0337 - acc: 0.9885 - val_loss: 0.0573 - val_acc: 0.9766\nEpoch 851/1000\n - 4s - loss: 0.0347 - acc: 0.9877 - val_loss: 0.0569 - val_acc: 0.9771\nEpoch 852/1000\n - 4s - loss: 0.0335 - acc: 0.9885 - val_loss: 0.0571 - val_acc: 0.9766\nEpoch 853/1000\n - 4s - loss: 0.0342 - acc: 0.9883 - val_loss: 0.0570 - val_acc: 0.9783\nEpoch 854/1000\n - 4s - loss: 0.0329 - acc: 0.9884 - val_loss: 0.0572 - val_acc: 0.9766\nEpoch 855/1000\n - 4s - loss: 0.0345 - acc: 0.9883 - val_loss: 0.0570 - val_acc: 0.9766\nEpoch 856/1000\n - 4s - loss: 0.0341 - acc: 0.9880 - val_loss: 0.0573 - val_acc: 0.9771\nEpoch 857/1000\n - 4s - loss: 0.0338 - acc: 0.9874 - val_loss: 0.0569 - val_acc: 0.9766\nEpoch 858/1000\n - 4s - loss: 0.0337 - acc: 0.9879 - val_loss: 0.0570 - val_acc: 0.9777\nEpoch 859/1000\n - 4s - loss: 0.0340 - acc: 0.9876 - val_loss: 0.0571 - val_acc: 0.9760\nEpoch 860/1000\n - 4s - loss: 0.0342 - acc: 0.9879 - val_loss: 0.0570 - val_acc: 0.9766\nEpoch 861/1000\n - 4s - loss: 0.0338 - acc: 0.9878 - val_loss: 0.0572 - val_acc: 0.9760\nEpoch 862/1000\n - 4s - loss: 0.0344 - acc: 0.9876 - val_loss: 0.0573 - val_acc: 0.9766\nEpoch 863/1000\n - 4s - loss: 0.0339 - acc: 0.9884 - val_loss: 0.0570 - val_acc: 0.9766\nEpoch 864/1000\n - 4s - loss: 0.0331 - acc: 0.9891 - val_loss: 0.0569 - val_acc: 0.9771\nEpoch 865/1000\n - 4s - loss: 0.0329 - acc: 0.9890 - val_loss: 0.0574 - val_acc: 0.9754\nEpoch 866/1000\n - 4s - loss: 0.0333 - acc: 0.9883 - val_loss: 0.0570 - val_acc: 0.9760\nEpoch 867/1000\n - 4s - loss: 0.0336 - acc: 0.9884 - val_loss: 0.0573 - val_acc: 0.9766\nEpoch 868/1000\n - 4s - loss: 0.0329 - acc: 0.9879 - val_loss: 0.0568 - val_acc: 0.9771\nEpoch 869/1000\n - 4s - loss: 0.0338 - acc: 0.9883 - val_loss: 0.0568 - val_acc: 0.9771\nEpoch 870/1000\n - 4s - loss: 0.0323 - acc: 0.9885 - val_loss: 0.0571 - val_acc: 0.9754\nEpoch 871/1000\n - 4s - loss: 0.0324 - acc: 0.9893 - val_loss: 0.0573 - val_acc: 0.9771\nEpoch 872/1000\n - 4s - loss: 0.0320 - acc: 0.9889 - val_loss: 0.0570 - val_acc: 0.9771\nEpoch 873/1000\n - 4s - loss: 0.0330 - acc: 0.9876 - val_loss: 0.0571 - val_acc: 0.9771\nEpoch 874/1000\n - 4s - loss: 0.0338 - acc: 0.9888 - val_loss: 0.0569 - val_acc: 0.9760\nEpoch 875/1000\n - 4s - loss: 0.0339 - acc: 0.9883 - val_loss: 0.0575 - val_acc: 0.9760\nEpoch 876/1000\n - 4s - loss: 0.0336 - acc: 0.9890 - val_loss: 0.0570 - val_acc: 0.9777\nEpoch 877/1000\n - 4s - loss: 0.0330 - acc: 0.9890 - val_loss: 0.0571 - val_acc: 0.9777\nEpoch 878/1000\n - 4s - loss: 0.0338 - acc: 0.9886 - val_loss: 0.0570 - val_acc: 0.9760\nEpoch 879/1000\n - 4s - loss: 0.0335 - acc: 0.9884 - val_loss: 0.0569 - val_acc: 0.9760\nEpoch 880/1000\n - 4s - loss: 0.0334 - acc: 0.9883 - val_loss: 0.0570 - val_acc: 0.9771\nEpoch 881/1000\n - 4s - loss: 0.0337 - acc: 0.9890 - val_loss: 0.0570 - val_acc: 0.9771\nEpoch 882/1000\n - 4s - loss: 0.0330 - acc: 0.9885 - val_loss: 0.0568 - val_acc: 0.9777\nEpoch 883/1000\n - 4s - loss: 0.0332 - acc: 0.9887 - val_loss: 0.0569 - val_acc: 0.9766\nEpoch 884/1000\n - 4s - loss: 0.0329 - acc: 0.9884 - val_loss: 0.0569 - val_acc: 0.9771\nEpoch 885/1000\n - 4s - loss: 0.0328 - acc: 0.9883 - val_loss: 0.0570 - val_acc: 0.9777\nEpoch 886/1000\n - 4s - loss: 0.0329 - acc: 0.9889 - val_loss: 0.0569 - val_acc: 0.9771\nEpoch 887/1000\n - 4s - loss: 0.0329 - acc: 0.9874 - val_loss: 0.0569 - val_acc: 0.9771\nEpoch 888/1000\n - 4s - loss: 0.0334 - acc: 0.9878 - val_loss: 0.0569 - val_acc: 0.9771\nEpoch 889/1000\n - 4s - loss: 0.0321 - acc: 0.9889 - val_loss: 0.0571 - val_acc: 0.9771\nEpoch 890/1000\n - 4s - loss: 0.0327 - acc: 0.9889 - val_loss: 0.0567 - val_acc: 0.9766\nEpoch 891/1000\n - 4s - loss: 0.0325 - acc: 0.9883 - val_loss: 0.0569 - val_acc: 0.9771\nEpoch 892/1000\n - 4s - loss: 0.0331 - acc: 0.9879 - val_loss: 0.0568 - val_acc: 0.9777\nEpoch 893/1000\n - 4s - loss: 0.0319 - acc: 0.9890 - val_loss: 0.0567 - val_acc: 0.9783\nEpoch 894/1000\n - 4s - loss: 0.0318 - acc: 0.9895 - val_loss: 0.0573 - val_acc: 0.9766\nEpoch 895/1000\n - 4s - loss: 0.0326 - acc: 0.9891 - val_loss: 0.0569 - val_acc: 0.9777\nEpoch 896/1000\n - 4s - loss: 0.0322 - acc: 0.9885 - val_loss: 0.0569 - val_acc: 0.9771\nEpoch 897/1000\n - 4s - loss: 0.0329 - acc: 0.9889 - val_loss: 0.0568 - val_acc: 0.9777\nEpoch 898/1000\n - 4s - loss: 0.0332 - acc: 0.9886 - val_loss: 0.0569 - val_acc: 0.9771\nEpoch 899/1000\n - 4s - loss: 0.0327 - acc: 0.9886 - val_loss: 0.0567 - val_acc: 0.9777\nEpoch 900/1000\n - 4s - loss: 0.0324 - acc: 0.9890 - val_loss: 0.0569 - val_acc: 0.9777\nEpoch 901/1000\n - 4s - loss: 0.0327 - acc: 0.9891 - val_loss: 0.0565 - val_acc: 0.9783\nEpoch 902/1000\n - 4s - loss: 0.0315 - acc: 0.9891 - val_loss: 0.0568 - val_acc: 0.9771\nEpoch 903/1000\n - 4s - loss: 0.0326 - acc: 0.9893 - val_loss: 0.0568 - val_acc: 0.9771\nEpoch 904/1000\n - 4s - loss: 0.0324 - acc: 0.9882 - val_loss: 0.0569 - val_acc: 0.9766\nEpoch 905/1000\n - 4s - loss: 0.0320 - acc: 0.9890 - val_loss: 0.0569 - val_acc: 0.9771\nEpoch 906/1000\n - 4s - loss: 0.0333 - acc: 0.9889 - val_loss: 0.0570 - val_acc: 0.9771\nEpoch 907/1000\n - 4s - loss: 0.0328 - acc: 0.9890 - val_loss: 0.0570 - val_acc: 0.9771\nEpoch 908/1000\n - 4s - loss: 0.0321 - acc: 0.9891 - val_loss: 0.0568 - val_acc: 0.9766\nEpoch 909/1000\n - 4s - loss: 0.0318 - acc: 0.9893 - val_loss: 0.0567 - val_acc: 0.9789\nEpoch 910/1000\n - 4s - loss: 0.0317 - acc: 0.9900 - val_loss: 0.0568 - val_acc: 0.9777\nEpoch 911/1000\n - 4s - loss: 0.0323 - acc: 0.9886 - val_loss: 0.0568 - val_acc: 0.9777\nEpoch 912/1000\n - 4s - loss: 0.0320 - acc: 0.9897 - val_loss: 0.0569 - val_acc: 0.9766\nEpoch 913/1000\n - 4s - loss: 0.0330 - acc: 0.9887 - val_loss: 0.0567 - val_acc: 0.9771\nEpoch 914/1000\n - 4s - loss: 0.0323 - acc: 0.9897 - val_loss: 0.0564 - val_acc: 0.9777\nEpoch 915/1000\n - 4s - loss: 0.0325 - acc: 0.9885 - val_loss: 0.0565 - val_acc: 0.9777\nEpoch 916/1000\n - 4s - loss: 0.0317 - acc: 0.9893 - val_loss: 0.0568 - val_acc: 0.9777\nEpoch 917/1000\n - 4s - loss: 0.0325 - acc: 0.9891 - val_loss: 0.0566 - val_acc: 0.9777\nEpoch 918/1000\n - 4s - loss: 0.0318 - acc: 0.9895 - val_loss: 0.0568 - val_acc: 0.9771\nEpoch 919/1000\n - 4s - loss: 0.0303 - acc: 0.9901 - val_loss: 0.0572 - val_acc: 0.9771\nEpoch 920/1000\n - 4s - loss: 0.0326 - acc: 0.9879 - val_loss: 0.0565 - val_acc: 0.9777\nEpoch 921/1000\n - 4s - loss: 0.0320 - acc: 0.9888 - val_loss: 0.0567 - val_acc: 0.9771\nEpoch 922/1000\n - 4s - loss: 0.0319 - acc: 0.9892 - val_loss: 0.0566 - val_acc: 0.9777\nEpoch 923/1000\n - 4s - loss: 0.0320 - acc: 0.9887 - val_loss: 0.0566 - val_acc: 0.9771\nEpoch 924/1000\n - 4s - loss: 0.0313 - acc: 0.9896 - val_loss: 0.0570 - val_acc: 0.9771\nEpoch 925/1000\n - 4s - loss: 0.0320 - acc: 0.9893 - val_loss: 0.0567 - val_acc: 0.9777\nEpoch 926/1000\n - 4s - loss: 0.0316 - acc: 0.9891 - val_loss: 0.0567 - val_acc: 0.9777\nEpoch 927/1000\n - 4s - loss: 0.0316 - acc: 0.9886 - val_loss: 0.0568 - val_acc: 0.9777\nEpoch 928/1000\n - 4s - loss: 0.0310 - acc: 0.9891 - val_loss: 0.0568 - val_acc: 0.9777\nEpoch 929/1000\n - 4s - loss: 0.0311 - acc: 0.9897 - val_loss: 0.0569 - val_acc: 0.9771\nEpoch 930/1000\n - 4s - loss: 0.0312 - acc: 0.9890 - val_loss: 0.0571 - val_acc: 0.9766\nEpoch 931/1000\n - 4s - loss: 0.0322 - acc: 0.9892 - val_loss: 0.0566 - val_acc: 0.9777\nEpoch 932/1000\n - 4s - loss: 0.0308 - acc: 0.9890 - val_loss: 0.0567 - val_acc: 0.9771\nEpoch 933/1000\n - 4s - loss: 0.0317 - acc: 0.9889 - val_loss: 0.0564 - val_acc: 0.9777\nEpoch 934/1000\n - 4s - loss: 0.0319 - acc: 0.9888 - val_loss: 0.0567 - val_acc: 0.9771\nEpoch 935/1000\n - 4s - loss: 0.0310 - acc: 0.9898 - val_loss: 0.0562 - val_acc: 0.9771\nEpoch 936/1000\n - 4s - loss: 0.0306 - acc: 0.9905 - val_loss: 0.0565 - val_acc: 0.9771\nEpoch 937/1000\n - 4s - loss: 0.0307 - acc: 0.9890 - val_loss: 0.0566 - val_acc: 0.9777\nEpoch 938/1000\n - 4s - loss: 0.0321 - acc: 0.9888 - val_loss: 0.0565 - val_acc: 0.9777\nEpoch 939/1000\n - 4s - loss: 0.0312 - acc: 0.9894 - val_loss: 0.0567 - val_acc: 0.9777\nEpoch 940/1000\n - 4s - loss: 0.0306 - acc: 0.9903 - val_loss: 0.0566 - val_acc: 0.9777\nEpoch 941/1000\n - 4s - loss: 0.0310 - acc: 0.9891 - val_loss: 0.0567 - val_acc: 0.9777\nEpoch 942/1000\n - 4s - loss: 0.0313 - acc: 0.9897 - val_loss: 0.0567 - val_acc: 0.9771\nEpoch 943/1000\n - 4s - loss: 0.0311 - acc: 0.9895 - val_loss: 0.0566 - val_acc: 0.9777\nEpoch 944/1000\n - 4s - loss: 0.0321 - acc: 0.9885 - val_loss: 0.0565 - val_acc: 0.9766\nEpoch 945/1000\n - 4s - loss: 0.0314 - acc: 0.9897 - val_loss: 0.0568 - val_acc: 0.9777\nEpoch 946/1000\n - 4s - loss: 0.0312 - acc: 0.9895 - val_loss: 0.0564 - val_acc: 0.9771\nEpoch 947/1000\n - 4s - loss: 0.0311 - acc: 0.9895 - val_loss: 0.0565 - val_acc: 0.9777\nEpoch 948/1000\n - 4s - loss: 0.0310 - acc: 0.9896 - val_loss: 0.0567 - val_acc: 0.9777\nEpoch 949/1000\n - 4s - loss: 0.0318 - acc: 0.9886 - val_loss: 0.0565 - val_acc: 0.9777\nEpoch 950/1000\n - 4s - loss: 0.0311 - acc: 0.9895 - val_loss: 0.0566 - val_acc: 0.9777\nEpoch 951/1000\n - 4s - loss: 0.0319 - acc: 0.9892 - val_loss: 0.0565 - val_acc: 0.9771\nEpoch 952/1000\n - 4s - loss: 0.0306 - acc: 0.9896 - val_loss: 0.0567 - val_acc: 0.9777\nEpoch 953/1000\n - 4s - loss: 0.0308 - acc: 0.9896 - val_loss: 0.0567 - val_acc: 0.9766\nEpoch 954/1000\n - 4s - loss: 0.0306 - acc: 0.9900 - val_loss: 0.0566 - val_acc: 0.9777\nEpoch 955/1000\n - 4s - loss: 0.0311 - acc: 0.9893 - val_loss: 0.0568 - val_acc: 0.9771\nEpoch 956/1000\n - 4s - loss: 0.0308 - acc: 0.9902 - val_loss: 0.0566 - val_acc: 0.9777\nEpoch 957/1000\n - 4s - loss: 0.0302 - acc: 0.9900 - val_loss: 0.0565 - val_acc: 0.9771\nEpoch 958/1000\n - 4s - loss: 0.0305 - acc: 0.9899 - val_loss: 0.0565 - val_acc: 0.9771\nEpoch 959/1000\n - 4s - loss: 0.0308 - acc: 0.9897 - val_loss: 0.0566 - val_acc: 0.9760\nEpoch 960/1000\n - 4s - loss: 0.0306 - acc: 0.9892 - val_loss: 0.0568 - val_acc: 0.9766\nEpoch 961/1000\n - 4s - loss: 0.0313 - acc: 0.9888 - val_loss: 0.0567 - val_acc: 0.9777\nEpoch 962/1000\n - 4s - loss: 0.0307 - acc: 0.9890 - val_loss: 0.0565 - val_acc: 0.9777\nEpoch 963/1000\n - 4s - loss: 0.0306 - acc: 0.9895 - val_loss: 0.0566 - val_acc: 0.9777\nEpoch 964/1000\n - 4s - loss: 0.0305 - acc: 0.9897 - val_loss: 0.0569 - val_acc: 0.9766\nEpoch 965/1000\n - 4s - loss: 0.0309 - acc: 0.9887 - val_loss: 0.0564 - val_acc: 0.9771\nEpoch 966/1000\n - 4s - loss: 0.0310 - acc: 0.9895 - val_loss: 0.0565 - val_acc: 0.9771\nEpoch 967/1000\n - 4s - loss: 0.0293 - acc: 0.9902 - val_loss: 0.0565 - val_acc: 0.9777\nEpoch 968/1000\n - 4s - loss: 0.0306 - acc: 0.9891 - val_loss: 0.0563 - val_acc: 0.9777\nEpoch 969/1000\n - 4s - loss: 0.0298 - acc: 0.9900 - val_loss: 0.0565 - val_acc: 0.9771\nEpoch 970/1000\n - 4s - loss: 0.0303 - acc: 0.9897 - val_loss: 0.0572 - val_acc: 0.9766\nEpoch 971/1000\n - 4s - loss: 0.0303 - acc: 0.9895 - val_loss: 0.0564 - val_acc: 0.9777\nEpoch 972/1000\n - 4s - loss: 0.0304 - acc: 0.9895 - val_loss: 0.0563 - val_acc: 0.9771\nEpoch 973/1000\n - 4s - loss: 0.0298 - acc: 0.9902 - val_loss: 0.0564 - val_acc: 0.9771\nEpoch 974/1000\n - 4s - loss: 0.0298 - acc: 0.9893 - val_loss: 0.0563 - val_acc: 0.9777\nEpoch 975/1000\n - 4s - loss: 0.0305 - acc: 0.9900 - val_loss: 0.0564 - val_acc: 0.9777\nEpoch 976/1000\n - 4s - loss: 0.0297 - acc: 0.9903 - val_loss: 0.0565 - val_acc: 0.9777\nEpoch 977/1000\n - 4s - loss: 0.0306 - acc: 0.9893 - val_loss: 0.0563 - val_acc: 0.9777\nEpoch 978/1000\n - 4s - loss: 0.0308 - acc: 0.9897 - val_loss: 0.0568 - val_acc: 0.9760\nEpoch 979/1000\n - 4s - loss: 0.0302 - acc: 0.9894 - val_loss: 0.0566 - val_acc: 0.9771\nEpoch 980/1000\n - 4s - loss: 0.0305 - acc: 0.9892 - val_loss: 0.0564 - val_acc: 0.9783\nEpoch 981/1000\n - 4s - loss: 0.0293 - acc: 0.9899 - val_loss: 0.0567 - val_acc: 0.9766\nEpoch 982/1000\n - 4s - loss: 0.0309 - acc: 0.9891 - val_loss: 0.0565 - val_acc: 0.9777\nEpoch 983/1000\n - 4s - loss: 0.0315 - acc: 0.9892 - val_loss: 0.0563 - val_acc: 0.9783\nEpoch 984/1000\n - 4s - loss: 0.0306 - acc: 0.9897 - val_loss: 0.0563 - val_acc: 0.9789\nEpoch 985/1000\n - 4s - loss: 0.0291 - acc: 0.9905 - val_loss: 0.0565 - val_acc: 0.9771\nEpoch 986/1000\n - 4s - loss: 0.0306 - acc: 0.9895 - val_loss: 0.0566 - val_acc: 0.9766\nEpoch 987/1000\n - 4s - loss: 0.0303 - acc: 0.9895 - val_loss: 0.0565 - val_acc: 0.9777\nEpoch 988/1000\n - 4s - loss: 0.0307 - acc: 0.9890 - val_loss: 0.0565 - val_acc: 0.9777\nEpoch 989/1000\n - 4s - loss: 0.0299 - acc: 0.9898 - val_loss: 0.0567 - val_acc: 0.9771\nEpoch 990/1000\n - 4s - loss: 0.0297 - acc: 0.9895 - val_loss: 0.0563 - val_acc: 0.9783\nEpoch 991/1000\n - 4s - loss: 0.0295 - acc: 0.9906 - val_loss: 0.0562 - val_acc: 0.9777\nEpoch 992/1000\n - 4s - loss: 0.0296 - acc: 0.9899 - val_loss: 0.0562 - val_acc: 0.9783\nEpoch 993/1000\n - 4s - loss: 0.0294 - acc: 0.9906 - val_loss: 0.0563 - val_acc: 0.9777\nEpoch 994/1000\n - 4s - loss: 0.0301 - acc: 0.9904 - val_loss: 0.0563 - val_acc: 0.9777\nEpoch 995/1000\n - 4s - loss: 0.0298 - acc: 0.9898 - val_loss: 0.0563 - val_acc: 0.9783\nEpoch 996/1000\n - 4s - loss: 0.0292 - acc: 0.9905 - val_loss: 0.0564 - val_acc: 0.9783\nEpoch 997/1000\n - 4s - loss: 0.0312 - acc: 0.9899 - val_loss: 0.0565 - val_acc: 0.9783\nEpoch 998/1000\n - 4s - loss: 0.0292 - acc: 0.9900 - val_loss: 0.0564 - val_acc: 0.9777\nEpoch 999/1000\n - 4s - loss: 0.0305 - acc: 0.9899 - val_loss: 0.0563 - val_acc: 0.9777\nEpoch 1000/1000\n - 4s - loss: 0.0301 - acc: 0.9896 - val_loss: 0.0565 - val_acc: 0.9777\nCPU times: user 46min 37s, sys: 4min 35s, total: 51min 12s\nWall time: 1h 10min 44s\n\n```\n\nIn [11]:\n\n```py\nwith open('history.json', 'w') as f:\n    json.dump(history.history, f)\n\nhistory_df = pd.DataFrame(history.history)\nhistory_df[['loss', 'val_loss']].plot()\nhistory_df[['acc', 'val_acc']].plot()\n\n```\n\nOut[11]:\n\n```\n<matplotlib.axes._subplots.AxesSubplot at 0x7f883b80ecf8>\n```\n\n![](keras-transfer-vgg16_files/__results___10_1.png)![](keras-transfer-vgg16_files/__results___10_2.png)In [12]:\n\n```py\n%%time\nX_tst = []\nTest_imgs = []\nfor img_id in tqdm_notebook(os.listdir(test_dir)):\n    X_tst.append(cv2.imread(test_dir + img_id))     \n    Test_imgs.append(img_id)\nX_tst = np.asarray(X_tst)\nX_tst = X_tst.astype('float32')\nX_tst /= 255\n\n```\n\n```\nCPU times: user 788 ms, sys: 544 ms, total: 1.33 s\nWall time: 5.92 s\n\n```\n\nIn [13]:\n\n```py\n# Prediction\ntest_predictions = model.predict(X_tst)\n\n```\n\nIn [14]:\n\n```py\nsub_df = pd.DataFrame(test_predictions, columns=['has_cactus'])\nsub_df['has_cactus'] = sub_df['has_cactus'].apply(lambda x: 1 if x > 0.75 else 0)\n\n```\n\nIn [15]:\n\n```py\nsub_df['id'] = ''\ncols = sub_df.columns.tolist()\ncols = cols[-1:] + cols[:-1]\nsub_df=sub_df[cols]\n\n```\n\nIn [16]:\n\n```py\nfor i, img in enumerate(Test_imgs):\n    sub_df.set_value(i,'id',img)\n\n```\n\n```\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n\n```\n\nIn [17]:\n\n```py\nsub_df.head()\n\n```\n\nOut[17]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 79ac4cc3b082e0a1defe1be601806efd.jpg | 1 |\n| --- | --- | --- |\n| 1 | e880364d6521c6f3a27748ec62b0e335.jpg | 1 |\n| --- | --- | --- |\n| 2 | 74912492b6cdf28c4bfb9c8e1d35af3e.jpg | 1 |\n| --- | --- | --- |\n| 3 | 078cfa961183b30693ea2f13f5ff6d17.jpg | 1 |\n| --- | --- | --- |\n| 4 | 7fd729184ef182899ce3e7a174fb9bc0.jpg | 1 |\n| --- | --- | --- |\n\nIn [18]:\n\n```py\nsub_df.to_csv('submission.csv',index=False)\n\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16.md",
    "content": "# PCA-MLP VS PCA-CNN , Focal Loss resnet50 vs Vgg16\n\n> Author: https://www.kaggle.com/rohandx1996\n\n> From: https://www.kaggle.com/rohandx1996/pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\nIn [1]:\n\n```py\n# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.\n\n```\n\n```\n['test', 'sample_submission.csv', 'train.csv', 'train']\n\n```\n\n# Introduction\n\nIn this short notebook I wanted to experiment with PCA and neural network implemented with Keras library. Also I would like to ilustrate how PCA decomposition works on data from this dataset. Most important thing, I would like hopefully to get some feedback to improve this method or find completely different approach.\n\nThanks to other authors for publishing their notebooks, I reused some parts of the code when I was looking for fiding nice solutions for problems I had on a way.\n\n# let's first start analyzing the data how much classes we are dealing , data is imbalance or not\n\nIn [2]:\n\n```py\ntrain_df=pd.read_csv(\"../input/train.csv\")\n\n```\n\nIn [3]:\n\n```py\ntrain_df.shape\n\n```\n\nOut[3]:\n\n```\n(17500, 2)\n```\n\nIn [4]:\n\n```py\ntrain_df.head()\n\n```\n\nOut[4]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |\n| --- | --- | --- |\n| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |\n| --- | --- | --- |\n| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |\n| --- | --- | --- |\n| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |\n| --- | --- | --- |\n| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |\n| --- | --- | --- |\n\nIn [5]:\n\n```py\nimport seaborn as sns \nimport matplotlib.pyplot as plt\n\n```\n\nIn [6]:\n\n```py\nsns.countplot(train_df[\"has_cactus\"])\n\n```\n\nOut[6]:\n\n```\n<matplotlib.axes._subplots.AxesSubplot at 0x7facb493d9b0>\n```\n\n![](pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16_files/__results___7_1.png)\n\n# so again dealing with imbalance classifacition we have to use some dice loss or focal loss or in keras we have to use logits_v2 with categorical cross entropy\n\nIn [7]:\n\n```py\nimport cv2\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport json\nimport os\nfrom tqdm import tqdm, tqdm_notebook\nfrom keras.models import Sequential\nfrom keras.layers import Activation, Dropout, Flatten, Dense\nfrom keras.applications import VGG16\nfrom keras.optimizers import Adam\n\n```\n\n```\nUsing TensorFlow backend.\n\n```\n\nIn [8]:\n\n```py\ntrain_dir = \"../input/train/train/\"\ntest_dir = \"../input/test/test/\"\n\n```\n\nIn [9]:\n\n```py\nX_tr = []\nY_tr = []\nimges = train_df['id'].values\nfor img_id in tqdm_notebook(imges):\n    X_tr.append(cv2.imread(train_dir + img_id,0))    \n    Y_tr.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])  \nX_tr = np.asarray(X_tr)\nX_tr = X_tr.astype('float32')\nX_tr /= 255\nY_tr = np.asarray(Y_tr)\n\n```\n\nIn [10]:\n\n```py\nX_tr=X_tr.reshape(-1,32,32,1)\n\n```\n\nIn [11]:\n\n```py\nY_tr.shape\n\n```\n\nOut[11]:\n\n```\n(17500,)\n```\n\n# First for applying PCA we need to convert rgb images to grayscale , same as well for test images we have to do\n\nImage has 32x32 pixels, so data has 1024 features. Now for computers such amount of data isn't that big, but there can be cases, when dimensional reduction can be important. It is better for further processing to have 625 features containing most of the data than 1000 features.\n\nBefore I use PCA to reduce dimensionality of the data I will standardize it using sklearn StandartScaler. It is fitted to train data, because I assume that I know nothing about test data. Both datasets are transformed.\n\nIn [12]:\n\n```py\nX_tr.shape\n\n```\n\nOut[12]:\n\n```\n(17500, 32, 32, 1)\n```\n\n# so now we need pixel wise values after that only we can apply PCA to see that with less features can we get same accuracy\n\nIn [13]:\n\n```py\ntarget=train_df[\"has_cactus\"]\n\n```\n\nIn [14]:\n\n```py\ntrain_df=train_df.drop(\"has_cactus\",axis=1)\n\n```\n\nIn [15]:\n\n```py\nimport os,array\nimport pandas as pd\nimport time\nimport dask as dd\n\nfrom PIL import Image\ndef pixelconv(file_list,img_height,img_width,pixels):  \n    columnNames = list()\n\n    for i in range(pixels):\n        pixel = 'pixel'\n        pixel += str(i)\n        columnNames.append(pixel)\n\n    train_data = pd.DataFrame(columns = columnNames)\n    start_time = time.time()\n    for i in tqdm_notebook(file_list):\n        t = i\n        img_name = t\n        img = Image.open('../input/train/train/'+img_name)\n        rawData = img.load()\n        #print rawData\n        data = []\n        for y in range(img_height):\n            for x in range(img_width):\n                data.append(rawData[x,y][0])\n        print (i)\n        k = 0\n        #print data\n        train_data.loc[i] = [data[k] for k in range(pixels)]\n    #print train_data.loc[0]\n\n    print (\"Done pixel values conversion\")\n    print  (time.time()-start_time)\n    print (train_data)\n    train_data.to_csv(\"train_converted_new.csv\",index = False)\n    print (\"Done data frame conversion\")\n    print  (time.time()-start_time)\npixelconv(train_df.id,32,32,1024) # pass pandas dataframe in which path of images only as column\n                                    # in return csv file will save in working directory 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 | pixel0 | pixel1 | pixel2 | pixel3 | pixel4 | pixel5 | pixel6 | pixel7 | pixel8 | pixel9 | pixel10 | pixel11 | pixel12 | pixel13 | pixel14 | pixel15 | pixel16 | pixel17 | pixel18 | pixel19 | pixel20 | pixel21 | pixel22 | pixel23 | pixel24 | pixel25 | pixel26 | pixel27 | pixel28 | pixel29 | pixel30 | pixel31 | pixel32 | pixel33 | pixel34 | pixel35 | pixel36 | pixel37 | pixel38 | pixel39 | ... | pixel984 | pixel985 | pixel986 | pixel987 | pixel988 | pixel989 | pixel990 | pixel991 | pixel992 | pixel993 | pixel994 | pixel995 | pixel996 | pixel997 | pixel998 | pixel999 | pixel1000 | pixel1001 | pixel1002 | pixel1003 | pixel1004 | pixel1005 | pixel1006 | pixel1007 | pixel1008 | pixel1009 | pixel1010 | pixel1011 | pixel1012 | pixel1013 | pixel1014 | pixel1015 | pixel1016 | pixel1017 | pixel1018 | pixel1019 | pixel1020 | pixel1021 | pixel1022 | pixel1023 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 0 | 136 | 134 | 147 | 136 | 116 | 100 | 100 | 116 | 102 | 125 | 120 | 120 | 126 | 134 | 136 | 137 | 132 | 93 | 137 | 149 | 100 | 128 | 144 | 171 | 163 | 162 | 146 | 144 | 147 | 152 | 147 | 157 | 124 | 161 | 158 | 96 | 122 | 114 | 121 | 94 | ... | 165 | 163 | 167 | 161 | 168 | 167 | 152 | 152 | 183 | 115 | 165 | 161 | 184 | 146 | 113 | 188 | 177 | 153 | 146 | 157 | 167 | 176 | 169 | 175 | 174 | 179 | 170 | 171 | 169 | 162 | 158 | 161 | 176 | 162 | 156 | 167 | 172 | 179 | 142 | 139 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 1 | 118 | 108 | 104 | 110 | 85 | 55 | 86 | 82 | 109 | 134 | 126 | 124 | 128 | 128 | 133 | 121 | 121 | 139 | 143 | 125 | 117 | 107 | 112 | 151 | 170 | 155 | 139 | 161 | 167 | 158 | 138 | 123 | 133 | 123 | 117 | 121 | 96 | 66 | 93 | 93 | ... | 106 | 104 | 127 | 141 | 127 | 113 | 140 | 183 | 93 | 65 | 60 | 61 | 69 | 119 | 121 | 93 | 100 | 151 | 206 | 246 | 216 | 177 | 154 | 90 | 54 | 89 | 107 | 128 | 148 | 147 | 105 | 94 | 121 | 141 | 124 | 154 | 143 | 104 | 97 | 126 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 2 | 158 | 158 | 159 | 168 | 145 | 121 | 142 | 134 | 155 | 137 | 168 | 168 | 96 | 141 | 184 | 95 | 160 | 195 | 178 | 132 | 104 | 153 | 149 | 130 | 124 | 121 | 122 | 124 | 125 | 166 | 142 | 133 | 169 | 145 | 173 | 163 | 147 | 124 | 133 | 140 | ... | 169 | 144 | 153 | 109 | 174 | 139 | 145 | 136 | 144 | 143 | 146 | 147 | 111 | 141 | 171 | 154 | 162 | 156 | 157 | 169 | 154 | 147 | 139 | 164 | 163 | 146 | 143 | 158 | 115 | 162 | 149 | 154 | 145 | 171 | 130 | 156 | 158 | 163 | 161 | 152 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 3 | 147 | 122 | 101 | 126 | 126 | 131 | 133 | 112 | 157 | 165 | 160 | 136 | 135 | 140 | 97 | 161 | 157 | 138 | 132 | 130 | 133 | 102 | 142 | 148 | 156 | 155 | 167 | 166 | 169 | 148 | 151 | 116 | 136 | 148 | 132 | 153 | 98 | 124 | 142 | 127 | ... | 167 | 151 | 99 | 153 | 183 | 93 | 165 | 146 | 152 | 124 | 136 | 145 | 139 | 157 | 148 | 158 | 119 | 138 | 137 | 102 | 154 | 85 | 139 | 196 | 55 | 171 | 117 | 182 | 141 | 143 | 139 | 145 | 67 | 134 | 181 | 148 | 108 | 139 | 133 | 138 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 4 | 113 | 109 | 123 | 156 | 154 | 128 | 119 | 119 | 101 | 79 | 129 | 147 | 125 | 139 | 136 | 131 | 141 | 141 | 141 | 135 | 146 | 131 | 139 | 143 | 141 | 129 | 129 | 142 | 146 | 138 | 133 | 168 | 131 | 128 | 132 | 149 | 152 | 134 | 114 | 125 | ... | 75 | 130 | 160 | 137 | 123 | 75 | 88 | 139 | 107 | 120 | 89 | 134 | 138 | 148 | 140 | 128 | 137 | 171 | 164 | 178 | 163 | 116 | 123 | 125 | 139 | 145 | 158 | 172 | 159 | 147 | 125 | 81 | 94 | 155 | 133 | 151 | 134 | 74 | 50 | 128 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n\n# so finally we have dataset of pixel values now we can apply PCA with pixeld data\n\nIn [18]:\n\n```py\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\n\nfrom keras.models import Sequential\nfrom keras.utils import np_utils\nfrom keras.layers import Dense, Dropout, GaussianNoise, Conv1D\nfrom keras.preprocessing.image import ImageDataGenerator\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline\n\n```\n\nIn [19]:\n\n```py\npca = PCA(n_components=500)\npca.fit(new_data)\n\nplt.plot(np.cumsum(pca.explained_variance_ratio_))\nplt.xlabel('Number of components')\nplt.ylabel('Cumulative explained variance')\n\n```\n\nOut[19]:\n\n```\nText(0, 0.5, 'Cumulative explained variance')\n```\n\n![](pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16_files/__results___25_1.png)\n\nIn plot above we can see that cumulative explained variance is very high near 500 and then it increases very slowly. That means that data describing changes is mostly contained in i guess 625 components. We need to evaluate trade-offs before we choose number of components we use further. I choose 625 to check how it will work as it seems to have most of the data.\n\n# so we come to know that with less number features we can explain the dataset\n\nIn [20]:\n\n```py\nNCOMPONENTS = 625\n\npca = PCA(n_components=NCOMPONENTS)\nX_pca_train = pca.fit_transform(new_data)\npca_std = np.std(X_pca_train)\nprint(X_pca_train.shape)\n\n```\n\n```\n(17500, 625)\n\n```\n\nIn [21]:\n\n```py\ninv_pca = pca.inverse_transform(X_pca_train)\n#inv_sc = scaler.inverse_transform(inv_pca)\n\n```\n\nIn [22]:\n\n```py\nX_pca_train_new=X_pca_train.reshape(X_pca_train.shape[0],25,25,1)\n\n```\n\nIn [23]:\n\n```py\nX_pca_train_new.shape\n\n```\n\nOut[23]:\n\n```\n(17500, 25, 25, 1)\n```\n\n# Apply MLP With PCA\n\nIn [24]:\n\n```py\nX_pca_train.shape ### this shape will be used in MLP\n\n```\n\nOut[24]:\n\n```\n(17500, 625)\n```\n\n# Neural network with Keras\n\nI implemened simple model of multilayer perceptron (MLP) neural network using Keras and experimented with it.\n\nUsing library is simple. First you need to create model instance and then add layers using models.add() method. First layer need to be set up for proper input dimension. Output layer needs to have proper output dimension and activation function. In between hidden layers can be added.\n\nDuring compilation parameters of loss function, optimizer and metrics need to be set depanding on problem.\n\nIn [25]:\n\n```py\nimport keras\nmodel = Sequential()\nlayers = 1\nunits = 128\n\nmodel.add(Dense(units, input_dim=NCOMPONENTS, activation='relu'))\nmodel.add(GaussianNoise(pca_std))\nmodel.add(Dense(units, activation='relu'))\nmodel.add(GaussianNoise(pca_std))\nmodel.add(Dropout(0.1))\nmodel.add(Dense(1, activation='sigmoid'))\n\nmodel.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(lr=1e-5), metrics=['acc'])\nhistory = model.fit(X_pca_train,target,\n          batch_size=32,\n          epochs=10,\n          verbose=1,\n          validation_split=0.15)\n\n#model.fit(X_pca_train, Y_train, epochs=100, batch_size=256, validation_split=0.15, verbose=2)\n\n```\n\n```\nWARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nColocations handled automatically by placer.\nWARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\nWARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.cast instead.\nTrain on 14875 samples, validate on 2625 samples\nEpoch 1/10\n14875/14875 [==============================] - 3s 180us/step - loss: 7.6863 - acc: 0.4876 - val_loss: 5.4468 - val_acc: 0.5684\nEpoch 2/10\n14875/14875 [==============================] - 1s 97us/step - loss: 6.6398 - acc: 0.5578 - val_loss: 4.1218 - val_acc: 0.6728\nEpoch 3/10\n14875/14875 [==============================] - 1s 98us/step - loss: 5.7543 - acc: 0.6112 - val_loss: 3.5392 - val_acc: 0.7181\nEpoch 4/10\n14875/14875 [==============================] - 1s 97us/step - loss: 5.2455 - acc: 0.6477 - val_loss: 3.6776 - val_acc: 0.7406\nEpoch 5/10\n14875/14875 [==============================] - 1s 97us/step - loss: 4.9131 - acc: 0.6711 - val_loss: 3.8335 - val_acc: 0.7501\nEpoch 6/10\n14875/14875 [==============================] - 1s 97us/step - loss: 4.7023 - acc: 0.6859 - val_loss: 3.9065 - val_acc: 0.7516\nEpoch 7/10\n14875/14875 [==============================] - 1s 97us/step - loss: 4.5000 - acc: 0.6994 - val_loss: 3.9499 - val_acc: 0.7505\nEpoch 8/10\n14875/14875 [==============================] - 1s 97us/step - loss: 4.2819 - acc: 0.7166 - val_loss: 3.9731 - val_acc: 0.7505\nEpoch 9/10\n14875/14875 [==============================] - 1s 98us/step - loss: 4.2099 - acc: 0.7234 - val_loss: 3.9770 - val_acc: 0.7505\nEpoch 10/10\n14875/14875 [==============================] - 1s 98us/step - loss: 4.1711 - acc: 0.7273 - val_loss: 3.9780 - val_acc: 0.7505\n\n```\n\nIn [26]:\n\n```py\nimport matplotlib.pyplot as plt\n%matplotlib inline\naccuracy = history.history['acc']\nval_accuracy = history.history['val_acc']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\nepochs = range(len(accuracy))\nplt.plot(epochs, accuracy, 'bo', label='Training accuracy')\nplt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\nplt.title('Training and validation accuracy')\nplt.legend()\nplt.figure()\nplt.plot(epochs, loss, 'bo', label='Training loss')\nplt.plot(epochs, val_loss, 'b', label='Validation loss')\nplt.title('Training and validation loss')\nplt.legend()\nplt.show()\n\n```\n\n![](pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16_files/__results___36_0.png)![](pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16_files/__results___36_1.png)\n\n# Applied CNN with PCA\n\nIn [27]:\n\n```py\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.layers.normalization import BatchNormalization\n\nbatch_size = 256\nnum_classes = 1\nepochs = 200\n\n#input image dimensions\nimg_rows, img_cols = 25, 25\n\nmodel = Sequential()\nmodel.add(Conv2D(64, kernel_size=(3, 3),\n                 activation='relu',\n                 input_shape=(25,25,1)))\nmodel.add(MaxPooling2D((2, 2)))\nmodel.add(Dropout(0.1))\nmodel.add(Conv2D(128, (3, 3), activation='relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Flatten())\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dropout(0.1))\nmodel.add(Dense(num_classes, activation='sigmoid'))\n\nmodel.compile(loss=keras.losses.binary_crossentropy,\n              optimizer=keras.optimizers.Adam(lr=1e-5),\n              metrics=['accuracy'])\n\n```\n\nIn [28]:\n\n```py\nhistory1 = model.fit(X_pca_train_new,target,\n          batch_size=batch_size,\n          epochs=200,\n          verbose=1,\n          validation_split=0.15)\n\n```\n\n```\nTrain on 14875 samples, validate on 2625 samples\nEpoch 1/200\n14875/14875 [==============================] - 2s 121us/step - loss: 0.7758 - acc: 0.7275 - val_loss: 0.4472 - val_acc: 0.7794\nEpoch 2/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.5467 - acc: 0.7747 - val_loss: 0.3774 - val_acc: 0.8057\nEpoch 3/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.4593 - acc: 0.7954 - val_loss: 0.3489 - val_acc: 0.8210\nEpoch 4/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.4079 - acc: 0.8056 - val_loss: 0.3348 - val_acc: 0.8389\nEpoch 5/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.3790 - acc: 0.8185 - val_loss: 0.3184 - val_acc: 0.8411\nEpoch 6/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.3582 - acc: 0.8282 - val_loss: 0.3175 - val_acc: 0.8556\nEpoch 7/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.3467 - acc: 0.8296 - val_loss: 0.3079 - val_acc: 0.8571\nEpoch 8/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.3296 - acc: 0.8407 - val_loss: 0.3022 - val_acc: 0.8663\nEpoch 9/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.3195 - acc: 0.8453 - val_loss: 0.2826 - val_acc: 0.8670\nEpoch 10/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.3097 - acc: 0.8514 - val_loss: 0.2763 - val_acc: 0.8728\nEpoch 11/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.3043 - acc: 0.8536 - val_loss: 0.2751 - val_acc: 0.8758\nEpoch 12/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.2907 - acc: 0.8616 - val_loss: 0.2669 - val_acc: 0.8815\nEpoch 13/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.2877 - acc: 0.8648 - val_loss: 0.2596 - val_acc: 0.8792\nEpoch 14/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.2812 - acc: 0.8651 - val_loss: 0.2613 - val_acc: 0.8815\nEpoch 15/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.2781 - acc: 0.8703 - val_loss: 0.2552 - val_acc: 0.8850\nEpoch 16/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.2691 - acc: 0.8764 - val_loss: 0.2486 - val_acc: 0.8869\nEpoch 17/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.2649 - acc: 0.8771 - val_loss: 0.2439 - val_acc: 0.8930\nEpoch 18/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.2593 - acc: 0.8818 - val_loss: 0.2462 - val_acc: 0.8937\nEpoch 19/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.2554 - acc: 0.8837 - val_loss: 0.2382 - val_acc: 0.8941\nEpoch 20/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.2561 - acc: 0.8848 - val_loss: 0.2353 - val_acc: 0.8949\nEpoch 21/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.2450 - acc: 0.8893 - val_loss: 0.2355 - val_acc: 0.8960\nEpoch 22/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.2468 - acc: 0.8879 - val_loss: 0.2300 - val_acc: 0.8987\nEpoch 23/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2402 - acc: 0.8924 - val_loss: 0.2333 - val_acc: 0.8983\nEpoch 24/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2407 - acc: 0.8946 - val_loss: 0.2261 - val_acc: 0.8983\nEpoch 25/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2354 - acc: 0.8953 - val_loss: 0.2283 - val_acc: 0.8998\nEpoch 26/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2289 - acc: 0.8985 - val_loss: 0.2222 - val_acc: 0.9010\nEpoch 27/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2272 - acc: 0.8986 - val_loss: 0.2201 - val_acc: 0.8987\nEpoch 28/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2252 - acc: 0.8995 - val_loss: 0.2190 - val_acc: 0.9025\nEpoch 29/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2163 - acc: 0.9051 - val_loss: 0.2176 - val_acc: 0.9032\nEpoch 30/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.2167 - acc: 0.9053 - val_loss: 0.2171 - val_acc: 0.9048\nEpoch 31/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2159 - acc: 0.9036 - val_loss: 0.2171 - val_acc: 0.9029\nEpoch 32/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.2152 - acc: 0.9061 - val_loss: 0.2148 - val_acc: 0.9063\nEpoch 33/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2103 - acc: 0.9076 - val_loss: 0.2201 - val_acc: 0.9029\nEpoch 34/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2080 - acc: 0.9110 - val_loss: 0.2146 - val_acc: 0.9040\nEpoch 35/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2119 - acc: 0.9054 - val_loss: 0.2110 - val_acc: 0.9093\nEpoch 36/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2083 - acc: 0.9116 - val_loss: 0.2111 - val_acc: 0.9067\nEpoch 37/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2038 - acc: 0.9121 - val_loss: 0.2123 - val_acc: 0.9070\nEpoch 38/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2014 - acc: 0.9120 - val_loss: 0.2102 - val_acc: 0.9086\nEpoch 39/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2005 - acc: 0.9129 - val_loss: 0.2090 - val_acc: 0.9109\nEpoch 40/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.2006 - acc: 0.9123 - val_loss: 0.2121 - val_acc: 0.9078\nEpoch 41/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1957 - acc: 0.9156 - val_loss: 0.2082 - val_acc: 0.9097\nEpoch 42/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1966 - acc: 0.9133 - val_loss: 0.2063 - val_acc: 0.9124\nEpoch 43/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1917 - acc: 0.9166 - val_loss: 0.2065 - val_acc: 0.9093\nEpoch 44/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1866 - acc: 0.9193 - val_loss: 0.2039 - val_acc: 0.9116\nEpoch 45/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.1895 - acc: 0.9205 - val_loss: 0.2044 - val_acc: 0.9086\nEpoch 46/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1865 - acc: 0.9197 - val_loss: 0.2036 - val_acc: 0.9097\nEpoch 47/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1834 - acc: 0.9226 - val_loss: 0.2044 - val_acc: 0.9097\nEpoch 48/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1822 - acc: 0.9224 - val_loss: 0.2055 - val_acc: 0.9097\nEpoch 49/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1809 - acc: 0.9226 - val_loss: 0.2009 - val_acc: 0.9124\nEpoch 50/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.1798 - acc: 0.9235 - val_loss: 0.2024 - val_acc: 0.9101\nEpoch 51/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.1778 - acc: 0.9241 - val_loss: 0.1998 - val_acc: 0.9128\nEpoch 52/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1766 - acc: 0.9252 - val_loss: 0.2009 - val_acc: 0.9109\nEpoch 53/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1765 - acc: 0.9235 - val_loss: 0.1984 - val_acc: 0.9124\nEpoch 54/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1709 - acc: 0.9269 - val_loss: 0.1983 - val_acc: 0.9112\nEpoch 55/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1739 - acc: 0.9254 - val_loss: 0.2020 - val_acc: 0.9086\nEpoch 56/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1700 - acc: 0.9293 - val_loss: 0.2006 - val_acc: 0.9116\nEpoch 57/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1642 - acc: 0.9310 - val_loss: 0.1996 - val_acc: 0.9120\nEpoch 58/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1631 - acc: 0.9294 - val_loss: 0.1978 - val_acc: 0.9112\nEpoch 59/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.1651 - acc: 0.9316 - val_loss: 0.1987 - val_acc: 0.9124\nEpoch 60/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1609 - acc: 0.9316 - val_loss: 0.2037 - val_acc: 0.9086\nEpoch 61/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1633 - acc: 0.9329 - val_loss: 0.1996 - val_acc: 0.9131\nEpoch 62/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1589 - acc: 0.9347 - val_loss: 0.1978 - val_acc: 0.9139\nEpoch 63/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1598 - acc: 0.9325 - val_loss: 0.1950 - val_acc: 0.9143\nEpoch 64/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1549 - acc: 0.9358 - val_loss: 0.1966 - val_acc: 0.9143\nEpoch 65/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1513 - acc: 0.9365 - val_loss: 0.1959 - val_acc: 0.9150\nEpoch 66/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1542 - acc: 0.9354 - val_loss: 0.2022 - val_acc: 0.9109\nEpoch 67/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1505 - acc: 0.9368 - val_loss: 0.1941 - val_acc: 0.9150\nEpoch 68/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1505 - acc: 0.9382 - val_loss: 0.1938 - val_acc: 0.9143\nEpoch 69/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1484 - acc: 0.9387 - val_loss: 0.1984 - val_acc: 0.9158\nEpoch 70/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1451 - acc: 0.9387 - val_loss: 0.1955 - val_acc: 0.9143\nEpoch 71/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1431 - acc: 0.9392 - val_loss: 0.1941 - val_acc: 0.9162\nEpoch 72/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1452 - acc: 0.9418 - val_loss: 0.1933 - val_acc: 0.9170\nEpoch 73/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1417 - acc: 0.9437 - val_loss: 0.1951 - val_acc: 0.9147\nEpoch 74/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1426 - acc: 0.9423 - val_loss: 0.1929 - val_acc: 0.9181\nEpoch 75/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1418 - acc: 0.9439 - val_loss: 0.1933 - val_acc: 0.9181\nEpoch 76/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1373 - acc: 0.9425 - val_loss: 0.1915 - val_acc: 0.9185\nEpoch 77/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1322 - acc: 0.9455 - val_loss: 0.1903 - val_acc: 0.9196\nEpoch 78/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1352 - acc: 0.9455 - val_loss: 0.1967 - val_acc: 0.9147\nEpoch 79/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1309 - acc: 0.9486 - val_loss: 0.1917 - val_acc: 0.9192\nEpoch 80/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1330 - acc: 0.9475 - val_loss: 0.1959 - val_acc: 0.9170\nEpoch 81/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1309 - acc: 0.9476 - val_loss: 0.1912 - val_acc: 0.9196\nEpoch 82/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1331 - acc: 0.9457 - val_loss: 0.1903 - val_acc: 0.9215\nEpoch 83/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1296 - acc: 0.9464 - val_loss: 0.1896 - val_acc: 0.9208\nEpoch 84/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1320 - acc: 0.9449 - val_loss: 0.1960 - val_acc: 0.9154\nEpoch 85/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1227 - acc: 0.9509 - val_loss: 0.1906 - val_acc: 0.9196\nEpoch 86/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1274 - acc: 0.9474 - val_loss: 0.1929 - val_acc: 0.9170\nEpoch 87/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1228 - acc: 0.9498 - val_loss: 0.1896 - val_acc: 0.9166\nEpoch 88/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1225 - acc: 0.9497 - val_loss: 0.1930 - val_acc: 0.9166\nEpoch 89/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1230 - acc: 0.9508 - val_loss: 0.1893 - val_acc: 0.9208\nEpoch 90/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1195 - acc: 0.9525 - val_loss: 0.1931 - val_acc: 0.9181\nEpoch 91/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1166 - acc: 0.9542 - val_loss: 0.1943 - val_acc: 0.9158\nEpoch 92/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.1158 - acc: 0.9545 - val_loss: 0.1920 - val_acc: 0.9185\nEpoch 93/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.1170 - acc: 0.9527 - val_loss: 0.1922 - val_acc: 0.9181\nEpoch 94/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1170 - acc: 0.9529 - val_loss: 0.1890 - val_acc: 0.9211\nEpoch 95/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1147 - acc: 0.9550 - val_loss: 0.1904 - val_acc: 0.9196\nEpoch 96/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1112 - acc: 0.9548 - val_loss: 0.1887 - val_acc: 0.9211\nEpoch 97/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1078 - acc: 0.9567 - val_loss: 0.1897 - val_acc: 0.9211\nEpoch 98/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1099 - acc: 0.9569 - val_loss: 0.1969 - val_acc: 0.9173\nEpoch 99/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1076 - acc: 0.9573 - val_loss: 0.1946 - val_acc: 0.9192\nEpoch 100/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1060 - acc: 0.9583 - val_loss: 0.1964 - val_acc: 0.9173\nEpoch 101/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1043 - acc: 0.9569 - val_loss: 0.1944 - val_acc: 0.9196\nEpoch 102/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1069 - acc: 0.9572 - val_loss: 0.2014 - val_acc: 0.9147\nEpoch 103/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1045 - acc: 0.9578 - val_loss: 0.1926 - val_acc: 0.9196\nEpoch 104/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1050 - acc: 0.9574 - val_loss: 0.1903 - val_acc: 0.9219\nEpoch 105/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1019 - acc: 0.9600 - val_loss: 0.1929 - val_acc: 0.9211\nEpoch 106/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.1012 - acc: 0.9591 - val_loss: 0.1933 - val_acc: 0.9211\nEpoch 107/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.1001 - acc: 0.9609 - val_loss: 0.2018 - val_acc: 0.9181\nEpoch 108/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.1014 - acc: 0.9609 - val_loss: 0.1948 - val_acc: 0.9211\nEpoch 109/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.0987 - acc: 0.9601 - val_loss: 0.1924 - val_acc: 0.9192\nEpoch 110/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.0963 - acc: 0.9613 - val_loss: 0.1921 - val_acc: 0.9192\nEpoch 111/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.0950 - acc: 0.9630 - val_loss: 0.1944 - val_acc: 0.9192\nEpoch 112/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0936 - acc: 0.9619 - val_loss: 0.1962 - val_acc: 0.9211\nEpoch 113/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0941 - acc: 0.9634 - val_loss: 0.1896 - val_acc: 0.9211\nEpoch 114/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0929 - acc: 0.9616 - val_loss: 0.1948 - val_acc: 0.9204\nEpoch 115/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0924 - acc: 0.9624 - val_loss: 0.1944 - val_acc: 0.9219\nEpoch 116/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0920 - acc: 0.9634 - val_loss: 0.1917 - val_acc: 0.9238\nEpoch 117/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0922 - acc: 0.9644 - val_loss: 0.1939 - val_acc: 0.9211\nEpoch 118/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0852 - acc: 0.9675 - val_loss: 0.1962 - val_acc: 0.9185\nEpoch 119/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0902 - acc: 0.9642 - val_loss: 0.1942 - val_acc: 0.9204\nEpoch 120/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0891 - acc: 0.9655 - val_loss: 0.1904 - val_acc: 0.9227\nEpoch 121/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0877 - acc: 0.9656 - val_loss: 0.2054 - val_acc: 0.9147\nEpoch 122/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0857 - acc: 0.9676 - val_loss: 0.1937 - val_acc: 0.9234\nEpoch 123/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0842 - acc: 0.9680 - val_loss: 0.1928 - val_acc: 0.9230\nEpoch 124/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0863 - acc: 0.9671 - val_loss: 0.1925 - val_acc: 0.9215\nEpoch 125/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0823 - acc: 0.9697 - val_loss: 0.1910 - val_acc: 0.9238\nEpoch 126/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0846 - acc: 0.9664 - val_loss: 0.1920 - val_acc: 0.9215\nEpoch 127/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0821 - acc: 0.9671 - val_loss: 0.1918 - val_acc: 0.9211\nEpoch 128/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0833 - acc: 0.9673 - val_loss: 0.1917 - val_acc: 0.9227\nEpoch 129/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0788 - acc: 0.9702 - val_loss: 0.1929 - val_acc: 0.9246\nEpoch 130/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0828 - acc: 0.9672 - val_loss: 0.1901 - val_acc: 0.9234\nEpoch 131/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0774 - acc: 0.9711 - val_loss: 0.2004 - val_acc: 0.9185\nEpoch 132/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0797 - acc: 0.9695 - val_loss: 0.1898 - val_acc: 0.9242\nEpoch 133/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0760 - acc: 0.9717 - val_loss: 0.1976 - val_acc: 0.9230\nEpoch 134/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0745 - acc: 0.9718 - val_loss: 0.2008 - val_acc: 0.9204\nEpoch 135/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0780 - acc: 0.9699 - val_loss: 0.2017 - val_acc: 0.9215\nEpoch 136/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0750 - acc: 0.9722 - val_loss: 0.1935 - val_acc: 0.9238\nEpoch 137/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0701 - acc: 0.9749 - val_loss: 0.1954 - val_acc: 0.9219\nEpoch 138/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0762 - acc: 0.9706 - val_loss: 0.1961 - val_acc: 0.9211\nEpoch 139/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0722 - acc: 0.9737 - val_loss: 0.1951 - val_acc: 0.9246\nEpoch 140/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0717 - acc: 0.9739 - val_loss: 0.1961 - val_acc: 0.9242\nEpoch 141/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0735 - acc: 0.9724 - val_loss: 0.1946 - val_acc: 0.9215\nEpoch 142/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0722 - acc: 0.9737 - val_loss: 0.1926 - val_acc: 0.9250\nEpoch 143/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0678 - acc: 0.9763 - val_loss: 0.1975 - val_acc: 0.9215\nEpoch 144/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.0693 - acc: 0.9741 - val_loss: 0.1965 - val_acc: 0.9230\nEpoch 145/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0672 - acc: 0.9754 - val_loss: 0.2021 - val_acc: 0.9223\nEpoch 146/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0700 - acc: 0.9747 - val_loss: 0.1967 - val_acc: 0.9227\nEpoch 147/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0672 - acc: 0.9745 - val_loss: 0.1922 - val_acc: 0.9284\nEpoch 148/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0660 - acc: 0.9748 - val_loss: 0.1976 - val_acc: 0.9230\nEpoch 149/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0684 - acc: 0.9754 - val_loss: 0.1989 - val_acc: 0.9223\nEpoch 150/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.0681 - acc: 0.9759 - val_loss: 0.1914 - val_acc: 0.9250\nEpoch 151/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0647 - acc: 0.9765 - val_loss: 0.1919 - val_acc: 0.9242\nEpoch 152/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0618 - acc: 0.9779 - val_loss: 0.1962 - val_acc: 0.9234\nEpoch 153/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0614 - acc: 0.9764 - val_loss: 0.1996 - val_acc: 0.9238\nEpoch 154/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0646 - acc: 0.9757 - val_loss: 0.2007 - val_acc: 0.9253\nEpoch 155/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.0639 - acc: 0.9776 - val_loss: 0.1942 - val_acc: 0.9253\nEpoch 156/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0611 - acc: 0.9776 - val_loss: 0.1960 - val_acc: 0.9234\nEpoch 157/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0613 - acc: 0.9772 - val_loss: 0.1993 - val_acc: 0.9219\nEpoch 158/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0611 - acc: 0.9779 - val_loss: 0.1948 - val_acc: 0.9246\nEpoch 159/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0557 - acc: 0.9808 - val_loss: 0.2034 - val_acc: 0.9211\nEpoch 160/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0595 - acc: 0.9777 - val_loss: 0.2054 - val_acc: 0.9215\nEpoch 161/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0610 - acc: 0.9771 - val_loss: 0.1947 - val_acc: 0.9238\nEpoch 162/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0560 - acc: 0.9797 - val_loss: 0.1975 - val_acc: 0.9211\nEpoch 163/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0593 - acc: 0.9794 - val_loss: 0.1953 - val_acc: 0.9227\nEpoch 164/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0554 - acc: 0.9811 - val_loss: 0.1993 - val_acc: 0.9211\nEpoch 165/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0541 - acc: 0.9816 - val_loss: 0.1977 - val_acc: 0.9230\nEpoch 166/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0563 - acc: 0.9808 - val_loss: 0.2029 - val_acc: 0.9215\nEpoch 167/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0570 - acc: 0.9794 - val_loss: 0.1997 - val_acc: 0.9230\nEpoch 168/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0576 - acc: 0.9792 - val_loss: 0.1982 - val_acc: 0.9227\nEpoch 169/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0574 - acc: 0.9788 - val_loss: 0.2032 - val_acc: 0.9230\nEpoch 170/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.0556 - acc: 0.9798 - val_loss: 0.1965 - val_acc: 0.9246\nEpoch 171/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0537 - acc: 0.9806 - val_loss: 0.2014 - val_acc: 0.9234\nEpoch 172/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0535 - acc: 0.9818 - val_loss: 0.1966 - val_acc: 0.9269\nEpoch 173/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.0532 - acc: 0.9806 - val_loss: 0.1973 - val_acc: 0.9284\nEpoch 174/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.0527 - acc: 0.9820 - val_loss: 0.2018 - val_acc: 0.9227\nEpoch 175/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.0513 - acc: 0.9822 - val_loss: 0.2034 - val_acc: 0.9219\nEpoch 176/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.0482 - acc: 0.9833 - val_loss: 0.1983 - val_acc: 0.9253\nEpoch 177/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.0519 - acc: 0.9807 - val_loss: 0.1973 - val_acc: 0.9272\nEpoch 178/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.0521 - acc: 0.9814 - val_loss: 0.2012 - val_acc: 0.9234\nEpoch 179/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.0487 - acc: 0.9828 - val_loss: 0.1944 - val_acc: 0.9265\nEpoch 180/200\n14875/14875 [==============================] - 1s 38us/step - loss: 0.0491 - acc: 0.9835 - val_loss: 0.2000 - val_acc: 0.9246\nEpoch 181/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.0468 - acc: 0.9847 - val_loss: 0.1972 - val_acc: 0.9280\nEpoch 182/200\n14875/14875 [==============================] - 1s 37us/step - loss: 0.0514 - acc: 0.9832 - val_loss: 0.2061 - val_acc: 0.9227\nEpoch 183/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0496 - acc: 0.9822 - val_loss: 0.1966 - val_acc: 0.9280\nEpoch 184/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0482 - acc: 0.9834 - val_loss: 0.2000 - val_acc: 0.9234\nEpoch 185/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0460 - acc: 0.9834 - val_loss: 0.1973 - val_acc: 0.9261\nEpoch 186/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0466 - acc: 0.9845 - val_loss: 0.1998 - val_acc: 0.9253\nEpoch 187/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0475 - acc: 0.9835 - val_loss: 0.2099 - val_acc: 0.9223\nEpoch 188/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0457 - acc: 0.9850 - val_loss: 0.2061 - val_acc: 0.9257\nEpoch 189/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0434 - acc: 0.9857 - val_loss: 0.2045 - val_acc: 0.9250\nEpoch 190/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0471 - acc: 0.9843 - val_loss: 0.2005 - val_acc: 0.9257\nEpoch 191/200\n14875/14875 [==============================] - 1s 36us/step - loss: 0.0433 - acc: 0.9855 - val_loss: 0.2001 - val_acc: 0.9261\nEpoch 192/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0463 - acc: 0.9836 - val_loss: 0.1993 - val_acc: 0.9276\nEpoch 193/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0446 - acc: 0.9843 - val_loss: 0.1996 - val_acc: 0.9265\nEpoch 194/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0429 - acc: 0.9866 - val_loss: 0.1992 - val_acc: 0.9257\nEpoch 195/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0415 - acc: 0.9864 - val_loss: 0.1980 - val_acc: 0.9269\nEpoch 196/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0405 - acc: 0.9861 - val_loss: 0.2056 - val_acc: 0.9265\nEpoch 197/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0424 - acc: 0.9860 - val_loss: 0.2027 - val_acc: 0.9246\nEpoch 198/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0427 - acc: 0.9864 - val_loss: 0.2046 - val_acc: 0.9238\nEpoch 199/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0391 - acc: 0.9877 - val_loss: 0.2002 - val_acc: 0.9257\nEpoch 200/200\n14875/14875 [==============================] - 1s 35us/step - loss: 0.0382 - acc: 0.9891 - val_loss: 0.2061 - val_acc: 0.9246\n\n```\n\nIn [29]:\n\n```py\nimport matplotlib.pyplot as plt\n%matplotlib inline\naccuracy = history1.history['acc']\nval_accuracy = history1.history['val_acc']\nloss = history1.history['loss']\nval_loss = history1.history['val_loss']\nepochs = range(len(accuracy))\nplt.plot(epochs, accuracy, 'bo', label='Training accuracy')\nplt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\nplt.title('CNN result Training and validation accuracy')\nplt.legend()\nplt.figure()\nplt.plot(epochs, loss, 'bo', label='Training loss')\nplt.plot(epochs, val_loss, 'b', label='Validation loss')\nplt.title('cnn Training and validation loss')\nplt.legend()\nplt.show()\n\n```\n\n![](pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16_files/__results___40_0.png)![](pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16_files/__results___40_1.png)\n\n# so we have pca with MLP as well as with CNN\n\nIn [30]:\n\n```py\n%%time\nX_tst = []\nTest_imgs = []\nfor img_id in tqdm_notebook(os.listdir(test_dir)):\n    X_tst.append(cv2.imread(test_dir + img_id,0))     \n    Test_imgs.append(img_id)\nX_tst = np.asarray(X_tst)\nX_tst = X_tst.astype('float32')\nX_tst /= 255\n\n```\n\n```\nCPU times: user 620 ms, sys: 624 ms, total: 1.24 s\nWall time: 8.4 s\n\n```\n\n# so now we will be again applyig PCA on test set and predict with cnn model let's how much accuracy we can bring\n\nIn [31]:\n\n```py\nX_tst.shape\n\n```\n\nOut[31]:\n\n```\n(4000, 32, 32)\n```\n\nIn [32]:\n\n```py\nX_tst=X_tst.reshape(-1,32,32,1)\n\n```\n\nIn [33]:\n\n```py\ntest_path=[]\nfor i in os.listdir(test_dir):\n    test_path.append(i)\n\n```\n\nIn [34]:\n\n```py\ntest_dataframe=pd.DataFrame(data=test_path,columns=[\"id\"])\n\n```\n\nIn [35]:\n\n```py\ntest_dataframe.head()\n\n```\n\nOut[35]:\n\n|  | id |\n| --- | --- |\n| 0 | 79ac4cc3b082e0a1defe1be601806efd.jpg |\n| --- | --- |\n| 1 | e880364d6521c6f3a27748ec62b0e335.jpg |\n| --- | --- |\n| 2 | 74912492b6cdf28c4bfb9c8e1d35af3e.jpg |\n| --- | --- |\n| 3 | 078cfa961183b30693ea2f13f5ff6d17.jpg |\n| --- | --- |\n| 4 | 7fd729184ef182899ce3e7a174fb9bc0.jpg |\n| --- | --- |\n\nIn [36]:\n\n```py\nimport os,array\nimport pandas as pd\nimport time\nimport dask as dd\n\nfrom PIL import Image\ndef pixelconv(file_list,img_height,img_width,pixels):  \n    columnNames = list()\n\n    for i in range(pixels):\n        pixel = 'pixel'\n        pixel += str(i)\n        columnNames.append(pixel)\n\n    train_data = pd.DataFrame(columns = columnNames)\n    start_time = time.time()\n    for i in file_list:\n        t = i\n        img_name = t\n        img = Image.open('../input/test/test/'+img_name)\n        rawData = img.load()\n        #print rawData\n        data = []\n        for y in range(img_height):\n            for x in range(img_width):\n                data.append(rawData[x,y][0])\n        print (i)\n        k = 0\n        #print data\n        train_data.loc[i] = [data[k] for k in range(pixels)]\n    #print train_data.loc[0]\n\n    print (\"Done pixel values conversion\")\n    print  (time.time()-start_time)\n    print (train_data)\n    train_data.to_csv(\"test_converted_new.csv\",index = False)\n    print (\"Done data frame conversion\")\n    print  (time.time()-start_time)\npixelconv(test_dataframe.id,32,32,1024) # pass pandas dataframe in which path of images only as column\n                                    # in return csv file will save in working directory \n\n```\n\n```\n79ac4cc3b082e0a1defe1be601806efd.jpg\ne880364d6521c6f3a27748ec62b0e335.jpg\n74912492b6cdf28c4bfb9c8e1d35af3e.jpg\n078cfa961183b30693ea2f13f5ff6d17.jpg\n7fd729184ef182899ce3e7a174fb9bc0.jpg\n2b5f23aba5af7bdffa13d7fc87cfd704.jpg\n56252603457e38c4b9d539d6a3be380d.jpg\n3a5657d140458ae32c2780818b51a0b2.jpg\n1bf2e11a8d218c3795202270a942705c.jpg\n60515ac7f73add1c96f360b21f45a944.jpg\na72f4468dfe498bcac525978f33efd44.jpg\n017396273d436137bbecaeb650dca415.jpg\n9abfa27735f7b07db4937edb849f6678.jpg\nce17e6f73cf2f24f914fd540cc7be31d.jpg\n01cd51bb115fe5c0c37acd8d8800613e.jpg\n6006eda8373b6ec4a7877a59e207db54.jpg\n2171d681af7ca40162e75d6541578cc4.jpg\n7a30d07990646562524e34978cf56217.jpg\n9e27b994a0d961611337ed9aa6f77da6.jpg\n9c413e5208f72035cbdf26060f07c283.jpg\nedae566dc88ad984072b19cc0f944bb6.jpg\n8fb20d993ed6d05d4906e7a9419ff0b3.jpg\n71f17376496d2d13e65c9558a790233e.jpg\n1c95e6e3c4286497cd9de8e21daf234c.jpg\nfdbe1d4bf0bc38a5020468a4e905f54d.jpg\n5731c1e30956ce3b6a399ef093d1952a.jpg\nd0e4ed7ec1b2ad782b331125e2585bac.jpg\nb3792528b28605c708318bff5643482c.jpg\ndca96ce805b92633e60414e5b85d851b.jpg\n0685ea9f53d3413582687efcdd3a9186.jpg\n085fe0fb3b4e8e5a291812fbdb435b0b.jpg\nd76ba07a4e35574429daa47efa608fde.jpg\n26daf2b88f9ff15eed5ec9b49a69e828.jpg\nd3ccc09a7c7c4ab2a7f461ffac9fa543.jpg\ne5bd18f9217f37b4480526b0d1e5779a.jpg\n0f3295196c41e09a2f69574ca4086cee.jpg\n28263ea6cfa7da3f460ccbdfc407d43b.jpg\n813d4aad604c6926d58b263c2d7b749e.jpg\n894fb4fb2ac63cec49e0871807be7686.jpg\n75210c002d520c35d4fc0058bfcef997.jpg\n0afb5d4940b8b1924249f732aa092edf.jpg\n6de2f9b5e2b940bb182d6123e7fd5a58.jpg\n09d14fdb544b681ab3d819135b09363f.jpg\nd9a0da40cf62a8e4a55da0a2800aa41e.jpg\na1869b511a601aa4a711db0cac2640ac.jpg\nf98afb5ffdd4c4204397d79ef2d03ad4.jpg\na92bc5f7ae3817c3fab98c00241633bd.jpg\n757b1a16df5a0b7ad3018246ced3bfa5.jpg\n59172078efef2121129f4d6b711c2ab3.jpg\n4cd37886d36c889b1c668eff7d4ad7ce.jpg\nc0937cf48a891a2ff08a4461ec5dde24.jpg\n4abaa3fc5b096607c2b82dc681481fec.jpg\n40f26f90a9c2b25d912ac6f8c2d4b661.jpg\n5a0be6a4c9aa672d943ea4e8d1f766db.jpg\nfdadd70d46f473bc81773484cd7698ce.jpg\n175b6c8c2c074a78943a3beec23e9829.jpg\nd4b11038aa760f13c5aaa22e8a4f622e.jpg\nb11b0bbd9c4ee17025409678ca99d41c.jpg\n9317b0cd4c7f01175f8c4cf9c14e0463.jpg\n811931817b6c0011610e5cbbc7ce73cf.jpg\n82b5359675054beac9841f9005ad1e97.jpg\nb6c8d14a142cecb4adf009611831a527.jpg\ne4bd460aec9b3eeabe3ee4362baed5e3.jpg\n1b33a4eef243ce68dc5bb196d0dae055.jpg\nb01ad733b1f9f5931deffce2e3aaecda.jpg\n308c8d0192436cfafa3f464463f00d4b.jpg\n5f85e91d75ae7cb6c28a482fd95408ee.jpg\na3d215c0cee2237026888c2aad9ce46c.jpg\n1813ca59db8b9c589432b2d2a13addbd.jpg\ncc172987571e00bc5b69176cd3c65b41.jpg\nc506db61e0fd015a044d8455224b85c9.jpg\ne5dab7d42d6f3372842affd1e372aa41.jpg\ndb63f5b5e6dd208b0274af861940ab79.jpg\n00e1e29865202c8ca715b0f14848d577.jpg\nb1ef14ac36996094fa9d5fb96929f920.jpg\n80d0f124d69060a1a6a055859d14dcb5.jpg\nda7babd8d22a59edcc8062800eed69cf.jpg\na9bb57aef76037fb4279fef085da7ded.jpg\n90e041f73045d30be92178e2d6781b3f.jpg\n0f009b8cafd6187ac665ff8ec81377c5.jpg\n5e059736237fad5474ba07958a414e56.jpg\n99f2b372b58210172e8a216715e03789.jpg\nfc748366c51fe0f145bfa9f9fb021656.jpg\nea369a6ecac2a429e979ed1fb29cbe7d.jpg\n47ec029469f28a1d2e5688646d3df9ef.jpg\n37b08e63469246f2d8ae584c8056497c.jpg\n8846d7d860ae1483dfde8a7553c6ef13.jpg\ne279a5ab7ca1e1af269284649669c91f.jpg\n7db6a3a73fc0ebee93a4bc07324f1806.jpg\nb4f7092d1f8411728fc1c8b93c784997.jpg\n0991b3c571f4572ca87c18496aa82ad9.jpg\n7af0162052240c315b5e394ff236d457.jpg\na4e7177e9d1a45dcb0ed7109cd2ca718.jpg\na6d3fbf99f75c1a85fe0967ac9f62aa4.jpg\n2ed195c82af8f75133c12059f09a8193.jpg\nc68a90e2738efcbc22118ab138382f25.jpg\n3c2504a050b6e034c09a07d9846bce16.jpg\nf6371b2054c8e2ca70ff8f8b95d89dda.jpg\n5ffc4f64d6f73f85c484596de8f68e1d.jpg\n48ca8050461b57fff93b243e4f0df798.jpg\n936b6a50cc8c784ca581c234d70b3ac1.jpg\n715cfe090c1baa5426e10a9e8dc7a253.jpg\ndfdf9bb6892ab408747a8e8b30a55d1e.jpg\n6bf99ba31777659fc4cb894a91804fd1.jpg\nc26793bf9e9678b899fac987012790cb.jpg\n8abcc02b98cd50f5ceb9457cc5ea5824.jpg\nbf0b2ca22e13755f48f6e165e0881f2b.jpg\n64a139f664174a9a74503936bd1b1e69.jpg\n1ac1330b23666244ca375ba28fc9cd0a.jpg\n8862730b54bf035d21eea92554b5231f.jpg\n22a27838e38eae93ffdb9a5f3d74f290.jpg\na710982eefc98829a0761d626bbc1941.jpg\n8fc8953116286c317d2996d0d4dd8daa.jpg\n161a8535dba455d1d45a8d41b2b75272.jpg\n68da05a9f553c6fe2933a94d8b7ab1a3.jpg\n3c049fe211b5435e0fd7ef2c8c5d9c80.jpg\n226e4d335afcad8d70b7a41e9d7d743a.jpg\nc9f3b58ee76f15511bc165f1878f43c7.jpg\n91b8ad22668d1f0c42fe39a5cceb3155.jpg\n19867a5025597f2ac036a809403b2b46.jpg\n2debf4cd47aab7a316eb9abe1a9812b9.jpg\nb628a98d7667eda3985fad80610237d0.jpg\n0e0b288e65f3f827789870ab83c3ec45.jpg\n2edb0bf826248b088d57e22799464c41.jpg\n8fd4fadcd4bcc5cb84729896306d11e1.jpg\n58db3c2f83688ddd0f5b6f33560ad991.jpg\n58f705102368eaad15841c30d2f7158e.jpg\n5eece65a36c835a22c0f98acc8649197.jpg\n6f29da2e19372c238ec14b592cb71710.jpg\nb1659cee93fbf8f8523f82c513035a27.jpg\n9db27681aba4e07736db8329be34de1a.jpg\n830976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pixel values conversion\n49.63109731674194\n                                     pixel0 pixel1    ...    pixel1022 pixel1023\n79ac4cc3b082e0a1defe1be601806efd.jpg    146    140    ...          149       143\ne880364d6521c6f3a27748ec62b0e335.jpg     69     66    ...           98        84\n74912492b6cdf28c4bfb9c8e1d35af3e.jpg    101     99    ...           90        96\n078cfa961183b30693ea2f13f5ff6d17.jpg    145    149    ...          141       182\n7fd729184ef182899ce3e7a174fb9bc0.jpg    120    129    ...          111       102\n2b5f23aba5af7bdffa13d7fc87cfd704.jpg    129    118    ...           99       107\n56252603457e38c4b9d539d6a3be380d.jpg     60     60    ...           61        64\n3a5657d140458ae32c2780818b51a0b2.jpg     64     62    ...           86        94\n1bf2e11a8d218c3795202270a942705c.jpg    105    112    ...           94       101\n60515ac7f73add1c96f360b21f45a944.jpg    150    144    ...          132       177\na72f4468dfe498bcac525978f33efd44.jpg     82     75    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       66\n1c95e6e3c4286497cd9de8e21daf234c.jpg    152    149    ...          149       153\nfdbe1d4bf0bc38a5020468a4e905f54d.jpg    162    124    ...          149        99\n5731c1e30956ce3b6a399ef093d1952a.jpg    146    146    ...          110       125\nd0e4ed7ec1b2ad782b331125e2585bac.jpg    145    145    ...          152       155\nb3792528b28605c708318bff5643482c.jpg    154    154    ...          173       167\ndca96ce805b92633e60414e5b85d851b.jpg    114    120    ...           86        85\n0685ea9f53d3413582687efcdd3a9186.jpg    103    119    ...          182       207\n...                                     ...    ...    ...          ...       ...\n06d0aecf78978460883a028fa5bd8b8a.jpg    101     82    ...           93        91\nc1bc236b00d9c99e6a4315b8ec03ce6d.jpg     89     92    ...           85        74\n6ba1cecaf3130cf3a5ebfa02f6b27574.jpg    135    125    ...          142       152\n2b40635cecf14d4695a3caca1b3fcc0e.jpg    101     93    ...           88        87\ndee276a5b593efc18f39fb00ca68c242.jpg    148    149    ...          177       185\nef9c8347bf86db13f42f0497e0f50ea3.jpg    141    116    ...          167       188\nc1a0f4eee85933112caefe7037586fc3.jpg    159    145    ...          196       169\n28f1df01c205dede3924ad332bc8849c.jpg    101     91    ...          124       107\n36ea7ab27d0d631bd68c93cec8ddb6aa.jpg     99    128    ...          105       104\n346ad07f2436ff12e653e50c3df48a4a.jpg     78     89    ...          107        75\n82b50e7749c74ca307c7874efdac1ce0.jpg    129    123    ...          104       104\ne6a8b275dc191bd9a277c261c593a640.jpg     91     96    ...           96       100\n96ad3d3813ea8ead501bb262d90e2a44.jpg    111    100    ...          115       106\n1288d1689880a9f0ad17c047575d85b8.jpg     72     91    ...          113       102\n2c696471585dffd4c73e5ab728bc095c.jpg     60     67    ...          146       150\na6c04a43ba6b9052c7373f43e943141b.jpg    118    130    ...          189       159\n8e6888ec1ad25a9d1826eff6dda21409.jpg     93     99    ...           75        59\n47567975e3a8075bdc3d65015a386442.jpg    158    149    ...          106       133\n97655c74928a8ba0e01a88401b552622.jpg     51     47    ...           93        47\n0a2339bcf84c99c90c95ff956f9245b4.jpg     68     69    ...          133       128\n518287d102fcb154fc3e487083b9c9f1.jpg     86     98    ...           56       108\n474ed48e38c6b36dfb19a539b9ece947.jpg    144    142    ...          196       186\na21f856603a60ec285ace0a722b533eb.jpg     10     47    ...           94       110\nd3c9c3a2e07ad3414e6c1bbde1ced2b0.jpg    100    102    ...           83        79\n69e4778a6a1da28b7ba84ed8d0e2e6cc.jpg    134    128    ...          145       136\n8b9e4e9898b36311d315db3ee8adddac.jpg     75     71    ...           37        51\n051d0b6becb641f28c99c7d9b9cc8bd8.jpg     83     87    ...          178       224\na715480e25a3178372affa70f34612d6.jpg     89     76    ...           90        86\nb1cc76011c5432d57156c5da95d6cee4.jpg    131    137    ...          210       216\ndf4f6a2f51e27b71089117a916d9d395.jpg     67     68    ...          148       151\n\n[4000 rows x 1024 columns]\nDone data frame conversion\n50.40800619125366\n\n```\n\nIn [37]:\n\n```py\nnew_test=pd.read_csv(\"../working/test_converted_new.csv\")\n\n```\n\nIn [38]:\n\n```py\nX_tst=pca.transform(new_test)\n\n```\n\nIn [39]:\n\n```py\nX_tst.shape\n\n```\n\nOut[39]:\n\n```\n(4000, 625)\n```\n\nIn [40]:\n\n```py\nX_tst=X_tst.reshape(-1,25,25,1)\n\n```\n\nIn [41]:\n\n```py\nX_tst.shape\n\n```\n\nOut[41]:\n\n```\n(4000, 25, 25, 1)\n```\n\n# CNN with Focal Loss , hey man i should foucs tuff example to classify\n\nThe focal loss was proposed for dense object detection task early this year. It enables training highly accurate dense object detectors with an imbalance between foreground and background classes at 1:1000 scale. This tutorial will show you how to apply focal loss to train a multi-class classifier model given highly imbalanced datasets.\n\nBackground Let's first take a look at other treatments for imbalanced datasets, and how focal loss comes to solve the issue.\n\nIn multi-class classification, a balanced dataset has target labels that are evenly distributed. If one class has overwhelmingly more samples than another, it can be seen as an imbalanced dataset. This imbalance causes two problems:\n\nTraining is inefficient as most samples are easy examples that contribute no useful learning signal; The easy examples can overwhelm training and lead to degenerate models. A common solution is to perform some form of hard negative mining that samples hard examples during training or more complex sampling/reweighing schemes.\n\nFor image classification specific, data augmentation techniques are also variable to create synthetic data for under-represented classes.\n\nThe focal loss is designed to address class imbalance by down-weighting inliers (easy examples) such that their contribution to the total loss is small even if their number is large. It focuses on training a sparse set of hard examples.\n\nIn [42]:\n\n```py\ntrain_df[\"image_location\"]=train_dir+train_df[\"id\"]\n\n```\n\nIn [43]:\n\n```py\nimport tensorflow as tf\nfrom keras import backend as K\n\n```\n\nIn [44]:\n\n```py\ndef binary_focal_loss(gamma=2., alpha=.25):\n    \"\"\"\n Binary form of focal loss.\n FL(p_t) = -alpha * (1 - p_t)**gamma * log(p_t)\n where p = sigmoid(x), p_t = p or 1 - p depending on if the label is 1 or 0, respectively.\n References:\n https://arxiv.org/pdf/1708.02002.pdf\n Usage:\n model.compile(loss=[binary_focal_loss(alpha=.25, gamma=2)], metrics=[\"accuracy\"], optimizer=adam)\n \"\"\"\n    def binary_focal_loss_fixed(y_true, y_pred):\n        \"\"\"\n :param y_true: A tensor of the same shape as `y_pred`\n :param y_pred:  A tensor resulting from a sigmoid\n :return: Output tensor.\n \"\"\"\n        pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))\n        pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))\n\n        epsilon = K.epsilon()\n        # clip to prevent NaN's and Inf's\n        pt_1 = K.clip(pt_1, epsilon, 1. - epsilon)\n        pt_0 = K.clip(pt_0, epsilon, 1. - epsilon)\n\n        return -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1)) \\\n               -K.sum((1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0))\n\n    return binary_focal_loss_fixed\n\n```\n\n# image augmentation we are using at runtime but to do that we need few things , i'm using a script to pass respective images in their respective label folder\n\n# so that script will work in smooth process , make unique set of labels folder first then image mapping we will using and pass every image in their respective folder\n\nIn [45]:\n\n```py\ntrain_df.head()\n\n```\n\nOut[45]:\n\n|  | id | image_location |\n| --- | --- | --- |\n| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | ../input/train/train/0004be2cfeaba1c0361d39e2b... |\n| --- | --- | --- |\n| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | ../input/train/train/000c8a36845c0208e833c79c1... |\n| --- | --- | --- |\n| 2 | 000d1e9a533f62e55c289303b072733d.jpg | ../input/train/train/000d1e9a533f62e55c289303b... |\n| --- | --- | --- |\n| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | ../input/train/train/0011485b40695e9138e92d0b3... |\n| --- | --- | --- |\n| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | ../input/train/train/0014d7a11e90b62848904c141... |\n| --- | --- | --- |\n\nIn [46]:\n\n```py\n#from console_progressbar import ProgressBar\nimport shutil\nimport tqdm\nimport os\nfilenames=list(train_df[\"image_location\"].values)\nlabels=list(train_df[\"has_cactus\"].values)\nfolders_to_be_created = np.unique(list(train_df['has_cactus'].values))\nfiles=[]\npath=\"../working/trainset/\"\nfor i in folders_to_be_created:\n    if not os.path.exists(path+str(i)):\n        os.makedirs(path+str(i)) \n#pb = ProgressBar(total=100, prefix='Save valid data', suffix='', decimals=3, length=50, fill='=')\nfor f in tqdm_notebook(range(len(filenames))):\n\n    current_image=filenames[f]\n    current_label=labels[f]\n    src_path=current_image\n\n    dst_path =path+str(current_label) \n\n    try :\n        shutil.copy(src_path, dst_path)\n        #pb.print_progress_bar((f + 1) * 100 / 4000)\n    except Exception as e :\n        files.append(src_path)\n\n```\n\n```\n---------------------------------------------------------------------------\nKeyError                                  Traceback (most recent call last)\n/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)\n 3077             try:\n-> 3078  return self._engine.get_loc(key)\n 3079             except KeyError:\n\npandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()\n\npandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()\n\npandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()\n\npandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()\n\nKeyError: 'has_cactus'\n\nDuring handling of the above exception, another exception occurred:\n\nKeyError                                  Traceback (most recent call last)\n<ipython-input-46-ea805c6c5d6d> in <module>()\n 4 import os\n 5 filenames=list(train_df[\"image_location\"].values)\n----> 6  labels=list(train_df[\"has_cactus\"].values)\n 7 folders_to_be_created = np.unique(list(train_df['has_cactus'].values))\n 8 files=[]\n\n/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py in __getitem__(self, key)\n 2686             return self._getitem_multilevel(key)\n 2687         else:\n-> 2688  return self._getitem_column(key)\n 2689 \n 2690     def _getitem_column(self, key):\n\n/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py in _getitem_column(self, key)\n 2693         # get column\n 2694         if self.columns.is_unique:\n-> 2695  return self._get_item_cache(key)\n 2696 \n 2697         # duplicate columns & possible reduce dimensionality\n\n/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py in _get_item_cache(self, item)\n 2487         res = cache.get(item)\n 2488         if res is None:\n-> 2489  values = self._data.get(item)\n 2490             res = self._box_item_values(item, values)\n 2491             cache[item] = res\n\n/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py in get(self, item, fastpath)\n 4113 \n 4114             if not isna(item):\n-> 4115  loc = self.items.get_loc(item)\n 4116             else:\n 4117                 indexer = np.arange(len(self.items))[isna(self.items)]\n\n/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)\n 3078                 return self._engine.get_loc(key)\n 3079             except KeyError:\n-> 3080  return self._engine.get_loc(self._maybe_cast_indexer(key))\n 3081 \n 3082         indexer = self.get_indexer([key], method=method, tolerance=tolerance)\n\npandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()\n\npandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()\n\npandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()\n\npandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()\n\nKeyError: 'has_cactus'\n```\n\n# image augumentation begins\n\n# we are using subset in imagedatagenerator therefore whatever augumentation we are applying will not get introduced in validation\n\nIn [47]:\n\n```py\nimport keras\nfrom keras_preprocessing.image import ImageDataGenerator\nfrom keras.applications.vgg16 import preprocess_input\ntrain_datagen = ImageDataGenerator(\n    rescale = 1./255,\n#     preprocessing_function= preprocess_input,\n    #shear_range=0.2,\n    zoom_range=0.2,\n    fill_mode = 'reflect',\n    #cval = 1,\n    rotation_range = 30,\n    width_shift_range=0.2,\n    height_shift_range=0.2,\n    horizontal_flip=True,validation_split=.20)\n\nvalid_datagen = ImageDataGenerator(rescale=1./255)#,preprocessing_function=preprocess_input)\n\ntrain_generator = train_datagen.flow_from_directory(\n    directory='../working/trainset/',\n    target_size=(32, 32),\n    batch_size=32,\n    class_mode='binary',subset=\"training\")\n\nvalidation_generator = train_datagen.flow_from_directory(\n    directory='../working/trainset/',\n    target_size=(32,32),\n    batch_size=32,\n    class_mode='binary',subset=\"validation\")\n\n```\n\n```\n---------------------------------------------------------------------------\nFileNotFoundError                         Traceback (most recent call last)\n<ipython-input-47-a4d87c2be586> in <module>()\n 20     target_size=(32, 32),\n 21     batch_size=32,\n---> 22 class_mode='binary',subset=\"training\") 23 \n 24 validation_generator = train_datagen.flow_from_directory(\n\n/opt/conda/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py in flow_from_directory(self, directory, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation)\n 536             follow_links=follow_links,\n 537             subset=subset,\n--> 538  interpolation=interpolation\n 539         )\n 540 \n\n/opt/conda/lib/python3.6/site-packages/keras_preprocessing/image/directory_iterator.py in __init__(self, directory, image_data_generator, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, data_format, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, dtype)\n 103         if not classes:\n 104             classes = []\n--> 105  for subdir in sorted(os.listdir(directory)):\n 106                 if os.path.isdir(os.path.join(directory, subdir)):\n 107                     classes.append(subdir)\n\nFileNotFoundError: [Errno 2] No such file or directory: '../working/trainset/'\n```\n\n# Applying VGG-16 First\n\nIn [48]:\n\n```py\nimport cv2\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport json\nimport os\nfrom tqdm import tqdm, tqdm_notebook\nfrom keras.models import Sequential\nfrom keras.layers import Activation, Dropout, Flatten, Dense\nfrom keras.applications import VGG16\nfrom keras.optimizers import Adam\n\n```\n\nIn [49]:\n\n```py\nvgg16_net = VGG16(weights='imagenet', \n                  include_top=False, \n                  input_shape=(32, 32, 3))\n\n```\n\n```\nDownloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n58892288/58889256 [==============================] - 2s 0us/step\n\n```\n\nIn [50]:\n\n```py\nvgg16_net.trainable = False\nvgg16_net.summary()\n\n```\n\n```\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_1 (InputLayer)         (None, 32, 32, 3)         0         \n_________________________________________________________________\nblock1_conv1 (Conv2D)        (None, 32, 32, 64)        1792      \n_________________________________________________________________\nblock1_conv2 (Conv2D)        (None, 32, 32, 64)        36928     \n_________________________________________________________________\nblock1_pool (MaxPooling2D)   (None, 16, 16, 64)        0         \n_________________________________________________________________\nblock2_conv1 (Conv2D)        (None, 16, 16, 128)       73856     \n_________________________________________________________________\nblock2_conv2 (Conv2D)        (None, 16, 16, 128)       147584    \n_________________________________________________________________\nblock2_pool (MaxPooling2D)   (None, 8, 8, 128)         0         \n_________________________________________________________________\nblock3_conv1 (Conv2D)        (None, 8, 8, 256)         295168    \n_________________________________________________________________\nblock3_conv2 (Conv2D)        (None, 8, 8, 256)         590080    \n_________________________________________________________________\nblock3_conv3 (Conv2D)        (None, 8, 8, 256)         590080    \n_________________________________________________________________\nblock3_pool (MaxPooling2D)   (None, 4, 4, 256)         0         \n_________________________________________________________________\nblock4_conv1 (Conv2D)        (None, 4, 4, 512)         1180160   \n_________________________________________________________________\nblock4_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n_________________________________________________________________\nblock4_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n_________________________________________________________________\nblock4_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n_________________________________________________________________\nblock5_conv1 (Conv2D)        (None, 2, 2, 512)         2359808   \n_________________________________________________________________\nblock5_conv2 (Conv2D)        (None, 2, 2, 512)         2359808   \n_________________________________________________________________\nblock5_conv3 (Conv2D)        (None, 2, 2, 512)         2359808   \n_________________________________________________________________\nblock5_pool (MaxPooling2D)   (None, 1, 1, 512)         0         \n=================================================================\nTotal params: 14,714,688\nTrainable params: 0\nNon-trainable params: 14,714,688\n_________________________________________________________________\n\n```\n\nIn [51]:\n\n```py\nmodel = Sequential()\nmodel.add(vgg16_net)\nmodel.add(Flatten())\nmodel.add(Dense(256))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(1))\nmodel.add(Activation('sigmoid'))\n\n```\n\nIn [52]:\n\n```py\nmodel.compile(loss=[binary_focal_loss(alpha=.25, gamma=2)], metrics=[\"accuracy\"], optimizer=Adam(lr=1e-5))\n\n```\n\nIn [53]:\n\n```py\nhistory=model.fit_generator(train_generator,\n                    steps_per_epoch = 14001//32,\n                    epochs=200,\n                    validation_data = validation_generator,validation_steps=3499//32)\n\n```\n\n```\n---------------------------------------------------------------------------\nNameError                                 Traceback (most recent call last)\n<ipython-input-53-fa7850284767> in <module>()\n----> 1 history=model.fit_generator(train_generator, 2                     steps_per_epoch = 14001//32,\n 3                     epochs=200,\n 4                     validation_data = validation_generator,validation_steps=3499//32)\n\nNameError: name 'train_generator' is not defined\n```\n\nIn [54]:\n\n```py\nimport matplotlib.pyplot as plt\nimport numpy as np\n#plot of model epochs what has been happened within 100 epochs baseline with vgg16 training 135 million params\n# freezing first layer\nfig = plt.figure(figsize=(12,8))\nplt.plot(history.history['acc'],'blue')\nplt.plot(history.history['val_acc'],'orange')\nplt.xticks(np.arange(0, 100, 10))\nplt.yticks(np.arange(0,1,.1))\nplt.rcParams['figure.figsize'] = (10, 10)\nplt.xlabel(\"Num of Epochs\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Training Accuracy vs Validation Accuracy\")\nplt.grid(True)\nplt.gray()\nplt.legend(['train','validation'])\nplt.show()\n\nplt.figure(1)\nplt.plot(history.history['loss'],'blue')\nplt.plot(history.history['val_loss'],'orange')\nplt.xticks(np.arange(0, 100, 10))\nplt.rcParams['figure.figsize'] = (10, 10)\nplt.xlabel(\"Num of Epochs\")\nplt.ylabel(\"Loss\")\nplt.title(\"Training Loss vs Validation Loss\")\nplt.grid(True)\nplt.gray()\nplt.legend(['train','validation'])\nplt.show()\n\n```\n\n![](pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16_files/__results___74_0.png)![](pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16_files/__results___74_1.png)In [55]:\n\n```py\n%%time\nX_tst = []\nTest_imgs = []\nfor img_id in tqdm_notebook(os.listdir(test_dir)):\n    X_tst.append(cv2.imread(test_dir + img_id))     \n    Test_imgs.append(img_id)\nX_tst = np.asarray(X_tst)\nX_tst = X_tst.astype('float32')\nX_tst /= 255\n\n```\n\n```\nCPU times: user 500 ms, sys: 368 ms, total: 868 ms\nWall time: 3.5 s\n\n```\n\nIn [56]:\n\n```py\n# Prediction\ntest_predictions = model.predict(X_tst)\n\n```\n\nIn [57]:\n\n```py\nsub_df = pd.DataFrame(test_predictions, columns=['has_cactus'])\nsub_df['has_cactus'] = sub_df['has_cactus'].apply(lambda x: 1 if x > 0.5 else 0)\n\n```\n\nIn [58]:\n\n```py\nsub_df['id'] = ''\ncols = sub_df.columns.tolist()\ncols = cols[-1:] + cols[:-1]\nsub_df=sub_df[cols]\n\n```\n\nIn [59]:\n\n```py\nfor i, img in enumerate(Test_imgs):\n    sub_df.set_value(i,'id',img)\n\n```\n\n```\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n\n```\n\nIn [60]:\n\n```py\nsub_df.head()\n\n```\n\nOut[60]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 79ac4cc3b082e0a1defe1be601806efd.jpg | 1 |\n| --- | --- | --- |\n| 1 | e880364d6521c6f3a27748ec62b0e335.jpg | 1 |\n| --- | --- | --- |\n| 2 | 74912492b6cdf28c4bfb9c8e1d35af3e.jpg | 1 |\n| --- | --- | --- |\n| 3 | 078cfa961183b30693ea2f13f5ff6d17.jpg | 1 |\n| --- | --- | --- |\n| 4 | 7fd729184ef182899ce3e7a174fb9bc0.jpg | 1 |\n| --- | --- | --- |\n\nIn [61]:\n\n```py\nsub_df.to_csv('submission.csv',index=False)\n\n```\n\n# Applying Resnet-50 and then after that we can compare results what we get from these models\n\nIn [62]:\n\n```py\nfrom tensorflow.python.keras.applications import ResNet50\nfrom tensorflow.python.keras.models import Sequential\nfrom tensorflow.python.keras.layers import Dense, Flatten, GlobalAveragePooling2D, BatchNormalization\nfrom tensorflow.python.keras.applications.resnet50 import preprocess_input\nfrom tensorflow.python.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.python.keras.preprocessing.image import load_img, img_to_array\n\n```\n\nIn [63]:\n\n```py\nmodel = Sequential()\n\nmodel.add(ResNet50(include_top=False, pooling='avg', weights='imagenet'))\nmodel.add(Flatten())\nmodel.add(BatchNormalization())\nmodel.add(Dense(2048, activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Dense(1024, activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Dense(1, activation='sigmoid'))\n\nmodel.layers[0].trainable = False\n\n```\n\n```\nDownloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n94658560/94653016 [==============================] - 2s 0us/step\n\n```\n\nIn [64]:\n\n```py\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n\n```\n\nIn [65]:\n\n```py\nhistory1=model.fit_generator(train_generator,\n                    steps_per_epoch = 14001//32,\n                    epochs=200,\n                    validation_data = validation_generator,validation_steps=3499//32)\n\n```\n\n```\n---------------------------------------------------------------------------\nNameError                                 Traceback (most recent call last)\n<ipython-input-65-4c7399f966aa> in <module>()\n----> 1 history1=model.fit_generator(train_generator, 2                     steps_per_epoch = 14001//32,\n 3                     epochs=200,\n 4                     validation_data = validation_generator,validation_steps=3499//32)\n\nNameError: name 'train_generator' is not defined\n```\n\n# Conclusion\n\nAfter experiments with parameters of the used models I came to the result of 0.89+ accuracy on the part of the test set, but I am unable to improve it.\n\nBecause dataset is build with images of handwritten digits getting bigger train set could help. Maybe I need to change approach, ignore PCA decomposition at all and use ImageDataGenerator to generate more images or use convolution layers.\n\nIf you know how above result can be (easily?) improved, please leave comment with suggestion. As data science newbie I would be grateful for any suggestions :)"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/simple-cnn-on-pytorch-for-beginers.md",
    "content": "# SImple CNN on PyTorch for beginers\n\n> Author: https://www.kaggle.com/bonhart\n\n> From: https://www.kaggle.com/bonhart/simple-cnn-on-pytorch-for-beginers\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\n**EDA** (Exploratory Data Analysis).\n\nThe purpose of EDA is:\n\n*   Look at the data\n*   Understand the distribution of two classes (hasn't cactus / has cactus)\n*   Look at some features of the image (distribution of RGB channels, average brightness, etc.)\n\nIn [1]:\n\n```py\n# Libreries\n\nimport numpy as np\nimport pandas as pd\nimport os\n\nimport cv2\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\n```\n\nIn [2]:\n\n```py\n# Data path\nlabels = pd.read_csv('../input/train.csv')\nsub = pd.read_csv('../input/sample_submission.csv')\ntrain_path = '../input/train/train/'\ntest_path = '../input/test/test/'\n\n```\n\nIn [3]:\n\n```py\nprint('Num train samples:{0}'.format(len(os.listdir(train_path))))\nprint('Num test samples:{0}'.format(len(os.listdir(test_path))))\n\n```\n\n```\nNum train samples:17500\nNum test samples:4000\n\n```\n\nIn [4]:\n\n```py\nlabels.head()\n\n```\n\nOut[4]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |\n| --- | --- | --- |\n| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |\n| --- | --- | --- |\n| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |\n| --- | --- | --- |\n| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |\n| --- | --- | --- |\n| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |\n| --- | --- | --- |\n\nIn [5]:\n\n```py\nlabels['has_cactus'].value_counts()\n\n```\n\nOut[5]:\n\n```\n1    13136\n0     4364\nName: has_cactus, dtype: int64\n```\n\nIn [6]:\n\n```py\nlab = 'Has cactus','Hasn\\'t cactus'\ncolors=['green','brown']\n\nplt.figure(figsize=(7,7))\nplt.pie(labels.groupby('has_cactus').size(), labels=lab,\n        labeldistance=1.1, autopct='%1.1f%%',\n        colors=colors,shadow=True, startangle=140)\nplt.show()\n\n```\n\n![](simple-cnn-on-pytorch-for-beginers_files/__results___6_0.png)\n\n**Has cactus**\n\nIn [7]:\n\n```py\nfig,ax = plt.subplots(1,5,figsize=(15,3))\n\nfor i, idx in enumerate(labels[labels['has_cactus']==1]['id'][-5:]):\n  path = os.path.join(train_path,idx)\n  ax[i].imshow(cv2.imread(path)) # [...,[2,1,0]]\n\n```\n\n![](simple-cnn-on-pytorch-for-beginers_files/__results___8_0.png)\n\n**Hasn't cactus**\n\nIn [8]:\n\n```py\nfig,ax = plt.subplots(1,5,figsize=(15,3))\n\nfor i, idx in enumerate(labels[labels['has_cactus']==0]['id'][-5:]):\n  path = os.path.join(train_path,idx)\n  ax[i].imshow(cv2.imread(path)) # [...,[2,1,0]]\n\n```\n\n![](simple-cnn-on-pytorch-for-beginers_files/__results___10_0.png)\n\n**convolutional neural network on pytorch from scratch**\n\nIn [9]:\n\n```py\n# Libreries\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import TensorDataset, DataLoader, Dataset\nimport torchvision\nimport torchvision.transforms as transforms\n\nfrom sklearn.model_selection import train_test_split\n\n```\n\nIn [10]:\n\n```py\n## Parameters for model\n\n# Hyper parameters\nnum_epochs = 25\nnum_classes = 2\nbatch_size = 128\nlearning_rate = 0.002\n\n# Device configuration\ndevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n\n```\n\nIn [11]:\n\n```py\n# data splitting\ntrain, val = train_test_split(labels, stratify=labels.has_cactus, test_size=0.1)\ntrain.shape, val.shape\n\n```\n\nOut[11]:\n\n```\n((15750, 2), (1750, 2))\n```\n\nChecking label distribution(must be 1:3)\n\nIn [12]:\n\n```py\ntrain['has_cactus'].value_counts()\n\n```\n\nOut[12]:\n\n```\n1    11822\n0     3928\nName: has_cactus, dtype: int64\n```\n\nIn [13]:\n\n```py\nval['has_cactus'].value_counts()\n\n```\n\nOut[13]:\n\n```\n1    1314\n0     436\nName: has_cactus, dtype: int64\n```\n\n**Simple custom generator**\n\nIn [14]:\n\n```py\n# NOTE: class is inherited from Dataset\nclass MyDataset(Dataset):\n    def __init__(self, df_data, data_dir = './', transform=None):\n        super().__init__()\n        self.df = df_data.values\n        self.data_dir = data_dir\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.df)\n\n    def __getitem__(self, index):\n        img_name,label = self.df[index]\n        img_path = os.path.join(self.data_dir, img_name)\n        image = cv2.imread(img_path)\n        if self.transform is not None:\n            image = self.transform(image)\n        return image, label\n\n```\n\nIn [15]:\n\n```py\n# Image preprocessing\ntrans_train = transforms.Compose([transforms.ToPILImage(),\n                                  transforms.Pad(32, padding_mode='reflect'),\n                                  transforms.ToTensor(),\n                                  transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5, 0.5, 0.5])])\n\ntrans_valid = transforms.Compose([transforms.ToPILImage(),\n                                  transforms.Pad(32, padding_mode='reflect'),\n                                  transforms.ToTensor(),\n                                  transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5, 0.5, 0.5])])\n\n# Data generators\ndataset_train = MyDataset(df_data=train, data_dir=train_path, transform=trans_train)\ndataset_valid = MyDataset(df_data=val, data_dir=train_path, transform=trans_valid)\n\nloader_train = DataLoader(dataset = dataset_train, batch_size=batch_size, shuffle=True, num_workers=0)\nloader_valid = DataLoader(dataset = dataset_valid, batch_size=batch_size//2, shuffle=False, num_workers=0)\n\n```\n\n**Model**\n\nIn [16]:\n\n```py\n# NOTE: class is inherited from nn.Module\nclass SimpleCNN(nn.Module):\n    def __init__(self):\n        # ancestor constructor call\n        super(SimpleCNN, self).__init__()\n\n        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=2)\n        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=2)\n        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=2)\n        self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=2)\n        self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=2)\n        self.bn1 = nn.BatchNorm2d(32)\n        self.bn2 = nn.BatchNorm2d(64)\n        self.bn3 = nn.BatchNorm2d(128)\n        self.bn4 = nn.BatchNorm2d(256)\n        self.bn5 = nn.BatchNorm2d(512)\n        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n        self.avg = nn.AvgPool2d(4)\n        self.fc = nn.Linear(512 * 1 * 1, 2) # !!!\n\n    def forward(self, x):\n        x = self.pool(F.leaky_relu(self.bn1(self.conv1(x)))) # first convolutional layer then batchnorm, then activation then pooling layer.\n        x = self.pool(F.leaky_relu(self.bn2(self.conv2(x))))\n        x = self.pool(F.leaky_relu(self.bn3(self.conv3(x))))\n        x = self.pool(F.leaky_relu(self.bn4(self.conv4(x))))\n        x = self.pool(F.leaky_relu(self.bn5(self.conv5(x))))\n        x = self.avg(x)\n        #print(x.shape) # lifehack to find out the correct dimension for the Linear Layer\n        x = x.view(-1, 512 * 1 * 1) # !!!\n        x = self.fc(x)\n        return x\n\n```\n\n**Important note:** You may notice that in lines with # !!! there is not very clear 128 * 11 * 11\\. This is the dimension of the picture before the FC layers (H x W x C), then you have to calculate it manually (in Keras, for example, .Flatten () does everything for you). However, there is one life hack — just make print (x.shape) in forward () (commented out line). You will see the size (batch_size, C, H, W) - you need to multiply everything except the first (batch_size), this will be the first dimension of Linear (), and it is in C H W that you need to \"expand\" x before feeding to Linear ().\n\nIn [17]:\n\n```py\nmodel = SimpleCNN().to(device)\n\n```\n\nIn [18]:\n\n```py\n# Loss and optimizer\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adamax(model.parameters(), lr=learning_rate)\n\n```\n\nIn [19]:\n\n```py\n# Train the model\ntotal_step = len(loader_train)\nfor epoch in range(num_epochs):\n    for i, (images, labels) in enumerate(loader_train):\n        images = images.to(device)\n        labels = labels.to(device)\n\n        # Forward pass\n        outputs = model(images)\n        loss = criterion(outputs, labels)\n\n        # Backward and optimize\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        if (i+1) % 100 == 0:\n            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' \n                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))\n\n```\n\n```\nEpoch [1/25], Step [100/124], Loss: 0.0584\nEpoch [2/25], Step [100/124], Loss: 0.0095\nEpoch [3/25], Step [100/124], Loss: 0.0156\nEpoch [4/25], Step [100/124], Loss: 0.0050\nEpoch [5/25], Step [100/124], Loss: 0.0051\nEpoch [6/25], Step [100/124], Loss: 0.0047\nEpoch [7/25], Step [100/124], Loss: 0.0112\nEpoch [8/25], Step [100/124], Loss: 0.0156\nEpoch [9/25], Step [100/124], Loss: 0.0078\nEpoch [10/25], Step [100/124], Loss: 0.0294\nEpoch [11/25], Step [100/124], Loss: 0.0055\nEpoch [12/25], Step [100/124], Loss: 0.0600\nEpoch [13/25], Step [100/124], Loss: 0.0016\nEpoch [14/25], Step [100/124], Loss: 0.0073\nEpoch [15/25], Step [100/124], Loss: 0.0053\nEpoch [16/25], Step [100/124], Loss: 0.0014\nEpoch [17/25], Step [100/124], Loss: 0.0036\nEpoch [18/25], Step [100/124], Loss: 0.0011\nEpoch [19/25], Step [100/124], Loss: 0.0030\nEpoch [20/25], Step [100/124], Loss: 0.0025\nEpoch [21/25], Step [100/124], Loss: 0.0012\nEpoch [22/25], Step [100/124], Loss: 0.0034\nEpoch [23/25], Step [100/124], Loss: 0.0060\nEpoch [24/25], Step [100/124], Loss: 0.0007\nEpoch [25/25], Step [100/124], Loss: 0.0010\n\n```\n\n**Accuracy Check**\n\nIn [20]:\n\n```py\n# Test the model\nmodel.eval()  # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)\nwith torch.no_grad():\n    correct = 0\n    total = 0\n    for images, labels in loader_valid:\n        images = images.to(device)\n        labels = labels.to(device)\n        outputs = model(images)\n        _, predicted = torch.max(outputs.data, 1)\n        total += labels.size(0)\n        correct += (predicted == labels).sum().item()\n\n    print('Test Accuracy of the model on the 1750 validation images: {} %'.format(100 * correct / total))\n\n# Save the model checkpoint\ntorch.save(model.state_dict(), 'model.ckpt')\n\n```\n\n```\nTest Accuracy of the model on the 1750 validation images: 99.25714285714285 %\n\n```\n\n**CSV submission**\n\nIn [21]:\n\n```py\n# generator for test data \ndataset_valid = MyDataset(df_data=sub, data_dir=test_path, transform=trans_valid)\nloader_test = DataLoader(dataset = dataset_valid, batch_size=32, shuffle=False, num_workers=0)\n\n```\n\nIn [22]:\n\n```py\nmodel.eval()\n\npreds = []\nfor batch_i, (data, target) in enumerate(loader_test):\n    data, target = data.cuda(), target.cuda()\n    output = model(data)\n\n    pr = output[:,1].detach().cpu().numpy()\n    for i in pr:\n        preds.append(i)\n\nsub['has_cactus'] = preds\nsub.to_csv('sub.csv', index=False)\n\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/simple-cnn.md",
    "content": "# Simple CNN\n\n> Author: https://www.kaggle.com/mariammohamed\n\n> From: https://www.kaggle.com/mariammohamed/simple-cnn\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\n> Score: 0.9995\n\nIn [1]:\n\n```py\n# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.\n\n```\n\n```\n['train', 'test', 'train.csv', 'sample_submission.csv']\n\n```\n\nIn [2]:\n\n```py\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport glob\nimport scipy\nimport cv2\n\nimport keras\n\n```\n\n```\nUsing TensorFlow backend.\n\n```\n\nIn [3]:\n\n```py\nimport random\n\n```\n\n**Exploration**\n\nIn [4]:\n\n```py\ntrain_data = pd.read_csv('../input/train.csv')\n\n```\n\nIn [5]:\n\n```py\ntrain_data.shape\n\n```\n\nOut[5]:\n\n```\n(17500, 2)\n```\n\nIn [6]:\n\n```py\ntrain_data.head()\n\n```\n\nOut[6]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |\n| --- | --- | --- |\n| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |\n| --- | --- | --- |\n| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |\n| --- | --- | --- |\n| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |\n| --- | --- | --- |\n| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |\n| --- | --- | --- |\n\nIn [7]:\n\n```py\ntrain_data.has_cactus.unique()\n\n```\n\nOut[7]:\n\n```\narray([1, 0])\n```\n\nIn [8]:\n\n```py\ntrain_data.has_cactus.hist()\n\n```\n\nOut[8]:\n\n```\n<matplotlib.axes._subplots.AxesSubplot at 0x7fedb9b7a080>\n```\n\n![](simple-cnn_files/__results___8_1.png)In [9]:\n\n```py\ntrain_data.has_cactus.value_counts()\n\n```\n\nOut[9]:\n\n```\n1    13136\n0     4364\nName: has_cactus, dtype: int64\n```\n\nIn [10]:\n\n```py\ntrain_data.has_cactus.plot()\n\n```\n\nOut[10]:\n\n```\n<matplotlib.axes._subplots.AxesSubplot at 0x7fedb9aa7f98>\n```\n\n![](simple-cnn_files/__results___10_1.png)\n\n**Model**\n\nIn [11]:\n\n```py\ndef image_generator2(batch_size = 16, all_data=True, shuffle=True, train=True, indexes=None):\n    while True:\n        if indexes is None:\n            if train:\n                if all_data:\n                    indexes = np.arange(train_data.shape[0])\n                else:\n                    indexes = np.arange(train_data[:15000].shape[0])\n                if shuffle:\n                    np.random.shuffle(indexes)\n            else:\n                indexes = np.arange(train_data[15000:].shape[0])\n\n        N = int(len(indexes) / batch_size)\n\n        # Read in each input, perform preprocessing and get labels\n        for i in range(N):\n            current_indexes = indexes[i*batch_size: (i+1)*batch_size]\n            batch_input = []\n            batch_output = [] \n            for index in current_indexes:\n                img = mpimg.imread('../input/train/train/' + train_data.id[index])\n                batch_input += [img]\n                batch_input += [img[::-1, :, :]]\n                batch_input += [img[:, ::-1, :]]\n                batch_input += [np.rot90(img)]\n\n                temp_img = np.zeros_like(img)\n                temp_img[:28, :, :] = img[4:, :, :]\n                batch_input += [temp_img]\n\n                temp_img = np.zeros_like(img)\n                temp_img[:, :28, :] = img[:, 4:, :]\n                batch_input += [temp_img]\n\n                temp_img = np.zeros_like(img)\n                temp_img[4:, :, :] = img[:28, :, :]\n                batch_input += [temp_img]\n\n                temp_img = np.zeros_like(img)\n                temp_img[:, 4:, :] = img[:, :28, :]\n                batch_input += [temp_img]\n\n                batch_input += [cv2.resize(img[2:30, 2:30, :], (32, 32))]\n\n                batch_input += [scipy.ndimage.interpolation.rotate(img, 10, reshape=False)]\n\n                batch_input += [scipy.ndimage.interpolation.rotate(img, 5, reshape=False)]\n\n                for _ in range(11):\n                    batch_output += [train_data.has_cactus[index]]\n\n            batch_input = np.array( batch_input )\n            batch_output = np.array( batch_output )\n\n            yield( batch_input, batch_output.reshape(-1, 1) )\n\n```\n\nIn [12]:\n\n```py\npositive_examples = train_data[train_data.has_cactus==1]\nnegative_examples = train_data[train_data.has_cactus==0]\n\n```\n\nIn [13]:\n\n```py\ndef augment_img(img):\n    batch_input = []\n    batch_input += [img]\n    batch_input += [img[::-1, :, :]]\n    batch_input += [img[:, ::-1, :]]\n    batch_input += [np.rot90(img)]\n\n    temp_img = np.zeros_like(img)\n    temp_img[:28, :, :] = img[4:, :, :]\n    batch_input += [temp_img]\n\n    temp_img = np.zeros_like(img)\n    temp_img[:, :28, :] = img[:, 4:, :]\n    batch_input += [temp_img]\n\n    temp_img = np.zeros_like(img)\n    temp_img[4:, :, :] = img[:28, :, :]\n    batch_input += [temp_img]\n\n    temp_img = np.zeros_like(img)\n    temp_img[:, 4:, :] = img[:, :28, :]\n    batch_input += [temp_img]\n\n    batch_input += [cv2.resize(img[2:30, 2:30, :], (32, 32))]\n\n    batch_input += [scipy.ndimage.interpolation.rotate(img, 10, reshape=False)]\n\n    batch_input += [scipy.ndimage.interpolation.rotate(img, 5, reshape=False)]\n\n    return batch_input\n\n```\n\nIn [14]:\n\n```py\ndef image_generator(batch_size = 8, all_data=True, shuffle=True, train=True, indexes=None):\n    while True:\n        if indexes is None:\n            if train:\n                indexes = positive_examples.index.tolist()\n                neg_indexes = negative_examples.index.tolist()\n                if shuffle:\n                    np.random.shuffle(indexes)\n                    np.random.shuffle(neg_indexes)\n\n        N = int(len(indexes) / (batch_size/2))\n        neg_N = int(len(neg_indexes) / (batch_size/2))\n\n        j = 0\n\n        # Read in each input, perform preprocessing and get labels\n        for i in range(N):\n            current_indexes = indexes[i*(batch_size//2): (i+1)*(batch_size//2)]\n            current_neg_indexes = neg_indexes[j*(batch_size//2): (j+1)*(batch_size//2)]\n            j = (j + 1) % neg_N\n            batch_input = []\n            batch_output = [] \n            for ind in range(len(current_indexes)):\n                index = current_indexes[ind]\n                neg_index = current_neg_indexes[ind]\n\n                img = mpimg.imread('../input/train/train/' + train_data.id[index])\n                batch_input.extend(augment_img(img))\n                for _ in range(11):\n                    batch_output += [train_data.has_cactus[index]]\n\n                neg_img = mpimg.imread('../input/train/train/' + train_data.id[neg_index])\n                batch_input.extend(augment_img(neg_img))\n                for _ in range(11):\n                    batch_output += [train_data.has_cactus[neg_index]]\n\n#                 factor = 0.05\n#                 new_img = factor*neg_img + (1-factor)*img\n#                 batch_input.append(new_img)\n#                 batch_output += [factor*train_data.has_cactus[neg_index]+(1-factor)*train_data.has_cactus[index]]\n\n#                 factor = 0.95\n#                 new_img = factor*neg_img + (1-factor)*img\n#                 batch_input.append(new_img)\n#                 batch_output += [factor*train_data.has_cactus[neg_index]+(1-factor)*train_data.has_cactus[index]]\n\n            batch_input = np.array( batch_input )\n            batch_output = np.array( batch_output )\n\n            yield( batch_input, batch_output.reshape(-1, 1) )\n\n```\n\nIn [15]:\n\n```py\nmodel = keras.models.Sequential()\nmodel.add(keras.layers.Conv2D(64, (5, 5), input_shape=(32, 32, 3)))\nmodel.add(keras.layers.BatchNormalization())\nmodel.add(keras.layers.LeakyReLU(alpha=0.3))\n\nmodel.add(keras.layers.Conv2D(64, (5, 5)))\nmodel.add(keras.layers.BatchNormalization())\nmodel.add(keras.layers.LeakyReLU(alpha=0.3))\n\nmodel.add(keras.layers.Conv2D(128, (5, 5)))\nmodel.add(keras.layers.BatchNormalization())\nmodel.add(keras.layers.LeakyReLU(alpha=0.3))\n\nmodel.add(keras.layers.Conv2D(128, (5, 5)))\nmodel.add(keras.layers.BatchNormalization())\nmodel.add(keras.layers.LeakyReLU(alpha=0.3))\n\nmodel.add(keras.layers.Conv2D(256, (3, 3)))\nmodel.add(keras.layers.BatchNormalization())\nmodel.add(keras.layers.LeakyReLU(alpha=0.3))\n\nmodel.add(keras.layers.Conv2D(256, (3, 3)))\nmodel.add(keras.layers.BatchNormalization())\nmodel.add(keras.layers.LeakyReLU(alpha=0.3))\n\nmodel.add(keras.layers.Conv2D(512, (3, 3)))\nmodel.add(keras.layers.BatchNormalization())\nmodel.add(keras.layers.LeakyReLU(alpha=0.3))\n\nmodel.add(keras.layers.Flatten())\n\nmodel.add(keras.layers.Dense(100))\nmodel.add(keras.layers.BatchNormalization())\nmodel.add(keras.layers.LeakyReLU(alpha=0.3))\n\nmodel.add(keras.layers.Dense(1, activation='sigmoid'))\n\n```\n\n```\nWARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nColocations handled automatically by placer.\n\n```\n\nIn [16]:\n\n```py\nmodel.summary()\n\n```\n\n```\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_1 (Conv2D)            (None, 28, 28, 64)        4864      \n_________________________________________________________________\nbatch_normalization_1 (Batch (None, 28, 28, 64)        256       \n_________________________________________________________________\nleaky_re_lu_1 (LeakyReLU)    (None, 28, 28, 64)        0         \n_________________________________________________________________\nconv2d_2 (Conv2D)            (None, 24, 24, 64)        102464    \n_________________________________________________________________\nbatch_normalization_2 (Batch (None, 24, 24, 64)        256       \n_________________________________________________________________\nleaky_re_lu_2 (LeakyReLU)    (None, 24, 24, 64)        0         \n_________________________________________________________________\nconv2d_3 (Conv2D)            (None, 20, 20, 128)       204928    \n_________________________________________________________________\nbatch_normalization_3 (Batch (None, 20, 20, 128)       512       \n_________________________________________________________________\nleaky_re_lu_3 (LeakyReLU)    (None, 20, 20, 128)       0         \n_________________________________________________________________\nconv2d_4 (Conv2D)            (None, 16, 16, 128)       409728    \n_________________________________________________________________\nbatch_normalization_4 (Batch (None, 16, 16, 128)       512       \n_________________________________________________________________\nleaky_re_lu_4 (LeakyReLU)    (None, 16, 16, 128)       0         \n_________________________________________________________________\nconv2d_5 (Conv2D)            (None, 14, 14, 256)       295168    \n_________________________________________________________________\nbatch_normalization_5 (Batch (None, 14, 14, 256)       1024      \n_________________________________________________________________\nleaky_re_lu_5 (LeakyReLU)    (None, 14, 14, 256)       0         \n_________________________________________________________________\nconv2d_6 (Conv2D)            (None, 12, 12, 256)       590080    \n_________________________________________________________________\nbatch_normalization_6 (Batch (None, 12, 12, 256)       1024      \n_________________________________________________________________\nleaky_re_lu_6 (LeakyReLU)    (None, 12, 12, 256)       0         \n_________________________________________________________________\nconv2d_7 (Conv2D)            (None, 10, 10, 512)       1180160   \n_________________________________________________________________\nbatch_normalization_7 (Batch (None, 10, 10, 512)       2048      \n_________________________________________________________________\nleaky_re_lu_7 (LeakyReLU)    (None, 10, 10, 512)       0         \n_________________________________________________________________\nflatten_1 (Flatten)          (None, 51200)             0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 100)               5120100   \n_________________________________________________________________\nbatch_normalization_8 (Batch (None, 100)               400       \n_________________________________________________________________\nleaky_re_lu_8 (LeakyReLU)    (None, 100)               0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 1)                 101       \n=================================================================\nTotal params: 7,913,625\nTrainable params: 7,910,609\nNon-trainable params: 3,016\n_________________________________________________________________\n\n```\n\nIn [17]:\n\n```py\nopt = keras.optimizers.SGD(lr=0.0001, momentum=0.9, nesterov=True)\nmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n\n```\n\nIn [18]:\n\n```py\ndef step_decay_schedule(initial_lr=1e-3, decay_factor=0.75, step_size=10):\n    '''\n Wrapper function to create a LearningRateScheduler with step decay schedule.\n '''\n    def schedule(epoch):\n        return initial_lr * (decay_factor ** np.floor(epoch/step_size))\n\n    return keras.callbacks.LearningRateScheduler(schedule)\n\nlr_sched = step_decay_schedule(initial_lr=1e-3, decay_factor=0.75, step_size=2)\nearly_stop = keras.callbacks.EarlyStopping(monitor='loss', patience=3)\n\nmodel.fit_generator(image_generator(), steps_per_epoch= train_data.shape[0] / 8, epochs=30, callbacks=[lr_sched, early_stop])\n\n```\n\n```\nWARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.cast instead.\nEpoch 1/30\n2188/2187 [==============================] - 86s 39ms/step - loss: 0.0982 - acc: 0.9642\nEpoch 2/30\n2188/2187 [==============================] - 81s 37ms/step - loss: 0.0414 - acc: 0.9855\nEpoch 3/30\n2188/2187 [==============================] - 80s 37ms/step - loss: 0.0200 - acc: 0.9931\nEpoch 4/30\n2188/2187 [==============================] - 80s 37ms/step - loss: 0.0122 - acc: 0.9961\nEpoch 5/30\n2188/2187 [==============================] - 80s 37ms/step - loss: 0.0066 - acc: 0.9982\nEpoch 6/30\n1897/2187 [=========================>....] - ETA: 10s - loss: 0.0050 - acc: 0.9988\n```\n\nIn [19]:\n\n```py\n# def step_decay_schedule(initial_lr=1e-3, decay_factor=0.75, step_size=10):\n#     '''\n#     Wrapper function to create a LearningRateScheduler with step decay schedule.\n#     '''\n#     def schedule(epoch):\n#         return initial_lr * (decay_factor ** np.floor(epoch/step_size))\n\n#     return keras.callbacks.LearningRateScheduler(schedule)\n\n# lr_sched = step_decay_schedule(initial_lr=1e-3, decay_factor=0.75, step_size=2)\n\n# model.fit_generator(image_generator(), steps_per_epoch= train_data.shape[0] / 8, epochs=20, callbacks=[lr_sched])\n\n```\n\nIn [20]:\n\n```py\nmodel.evaluate_generator(image_generator2(), steps=train_data.shape[0]//16)\n\n```\n\nOut[20]:\n\n```\n[0.009791018411583233, 0.9962779681069635]\n```\n\nIn [21]:\n\n```py\n# model.evaluate_generator(image_generator(), steps=train_data.shape[0]//8)\n\n```\n\nIn [22]:\n\n```py\n# keras.backend.eval(model.optimizer.lr.assign(0.00001))\n\n```\n\nIn [23]:\n\n```py\n# model.fit_generator(image_generator(), steps_per_epoch= train_data.shape[0] / 16, epochs=15)\n\n```\n\nIn [24]:\n\n```py\nindexes = np.arange(train_data.shape[0])\nN = int(len(indexes) / 64)   \nbatch_size = 64\n\nwrong_ind = []\nfor i in range(N):\n            current_indexes = indexes[i*64: (i+1)*64]\n            batch_input = []\n            batch_output = [] \n            for index in current_indexes:\n                img = mpimg.imread('../input/train/train/' + train_data.id[index])\n                batch_input += [img]\n                batch_output.append(train_data.has_cactus[index])\n\n            batch_input = np.array( batch_input )\n#             batch_output = np.array( batch_output )\n\n            model_pred = model.predict_classes(batch_input)\n            for j in range(len(batch_output)):\n                if model_pred[j] != batch_output[j]:\n                    wrong_ind.append(i*batch_size+j)\n\n```\n\nIn [25]:\n\n```py\nlen(wrong_ind)\n\n```\n\nOut[25]:\n\n```\n28\n```\n\nIn [26]:\n\n```py\nindexes = np.arange(train_data.shape[0])\nN = int(len(indexes) / 64)   \nbatch_size = 64\n\nwrong_ind = []\nfor i in range(N):\n            current_indexes = indexes[i*64: (i+1)*64]\n            batch_input = []\n            batch_output = [] \n            for index in current_indexes:\n                img = mpimg.imread('../input/train/train/' + train_data.id[index])\n                batch_input += [img[::-1, :, :]]\n                batch_output.append(train_data.has_cactus[index])\n\n            batch_input = np.array( batch_input )\n\n            model_pred = model.predict_classes(batch_input)\n            for j in range(len(batch_output)):\n                if model_pred[j] != batch_output[j]:\n                    wrong_ind.append(i*batch_size+j)\n\n```\n\nIn [27]:\n\n```py\nlen(wrong_ind)\n\n```\n\nOut[27]:\n\n```\n60\n```\n\nIn [28]:\n\n```py\nindexes = np.arange(train_data.shape[0])\nN = int(len(indexes) / 64)   \nbatch_size = 64\n\nwrong_ind = []\nfor i in range(N):\n            current_indexes = indexes[i*64: (i+1)*64]\n            batch_input = []\n            batch_output = [] \n            for index in current_indexes:\n                img = mpimg.imread('../input/train/train/' + train_data.id[index])\n                batch_input += [img[:, ::-1, :]]\n                batch_output.append(train_data.has_cactus[index])\n\n            batch_input = np.array( batch_input )\n\n            model_pred = model.predict_classes(batch_input)\n            for j in range(len(batch_output)):\n                if model_pred[j] != batch_output[j]:\n                    wrong_ind.append(i*batch_size+j)\n\n```\n\nIn [29]:\n\n```py\nlen(wrong_ind)\n\n```\n\nOut[29]:\n\n```\n71\n```\n\nIn [30]:\n\n```py\n!ls ../input/test/test/* | wc -l\n\n```\n\n```\n4000\n\n```\n\nIn [31]:\n\n```py\ntest_files = os.listdir('../input/test/test/')\n\n```\n\nIn [32]:\n\n```py\nlen(test_files)\n\n```\n\nOut[32]:\n\n```\n4000\n```\n\nIn [33]:\n\n```py\nbatch = 40\nall_out = []\nfor i in range(int(4000/batch)):\n    images = []\n    for j in range(batch):\n        img = mpimg.imread('../input/test/test/'+test_files[i*batch + j])\n        images += [img]\n    out = model.predict(np.array(images))\n    all_out += [out]\n\n```\n\nIn [34]:\n\n```py\nall_out = np.array(all_out).reshape((-1, 1))\n\n```\n\nIn [35]:\n\n```py\nall_out.shape\n\n```\n\nOut[35]:\n\n```\n(4000, 1)\n```\n\nIn [36]:\n\n```py\nsub_file = pd.DataFrame(data = {'id': test_files, 'has_cactus': all_out.reshape(-1).tolist()})\n\n```\n\nIn [37]:\n\n```py\nsub_file.to_csv('sample_submission.csv', index=False)\n\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/simple-fastai-exercise.md",
    "content": "# Simple_FastAI_exercise\n\n> Author: https://www.kaggle.com/kenseitrg\n\n> From: https://www.kaggle.com/kenseitrg/simple-fastai-exercise\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\n> Score: 1.0000\n\nIn [1]:\n\n```py\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n```\n\nIn [2]:\n\n```py\nfrom pathlib import Path\nfrom fastai import *\nfrom fastai.vision import *\nimport torch\n\n```\n\nIn [3]:\n\n```py\ndata_folder = Path(\"../input\")\n#data_folder.ls()\n\n```\n\nIn [4]:\n\n```py\ntrain_df = pd.read_csv(\"../input/train.csv\")\ntest_df = pd.read_csv(\"../input/sample_submission.csv\")\n\n```\n\nIn [5]:\n\n```py\ntest_img = ImageList.from_df(test_df, path=data_folder/'test', folder='test')\ntrfm = get_transforms(do_flip=True, flip_vert=True, max_rotate=10.0, max_zoom=1.1, max_lighting=0.2, max_warp=0.2, p_affine=0.75, p_lighting=0.75)\ntrain_img = (ImageList.from_df(train_df, path=data_folder/'train', folder='train')\n        .split_by_rand_pct(0.01)\n        .label_from_df()\n        .add_test(test_img)\n        .transform(trfm, size=128)\n        .databunch(path='.', bs=64, device= torch.device('cuda:0'))\n        .normalize(imagenet_stats)\n       )\n\n```\n\nIn [6]:\n\n```py\n#train_img.show_batch(rows=3, figsize=(7,6))\n\n```\n\nIn [7]:\n\n```py\nlearn = cnn_learner(train_img, models.densenet161, metrics=[error_rate, accuracy])\n\n```\n\n```\nDownloading: \"https://download.pytorch.org/models/densenet161-8d451a50.pth\" to /tmp/.torch/models/densenet161-8d451a50.pth\n115730790it [00:06, 17660476.43it/s]\n\n```\n\nIn [8]:\n\n```py\n#learn.lr_find()\n#learn.recorder.plot()\n\n```\n\nIn [9]:\n\n```py\nlr = 3e-02\nlearn.fit_one_cycle(5, slice(lr))\n\n```\n\nTotal time: 06:22\n\n| epoch | train_loss | valid_loss | error_rate | accuracy | time |\n| --- | --- | --- | --- | --- | --- |\n| 0 | 0.064186 | 0.090407 | 0.017143 | 0.982857 | 01:25 |\n| 1 | 0.039902 | 0.001073 | 0.000000 | 1.000000 | 01:15 |\n| 2 | 0.027812 | 0.003891 | 0.000000 | 1.000000 | 01:13 |\n| 3 | 0.012305 | 0.000548 | 0.000000 | 1.000000 | 01:14 |\n| 4 | 0.002986 | 0.001019 | 0.000000 | 1.000000 | 01:14 |\n\nIn [10]:\n\n```py\n#learn.unfreeze()\n#learn.lr_find()\n#learn.recorder.plot()\n\n```\n\nIn [11]:\n\n```py\n#learn.fit_one_cycle(1, slice(1e-06))\n\n```\n\nIn [12]:\n\n```py\n#interp = ClassificationInterpretation.from_learner(learn)\n#interp.plot_top_losses(9, figsize=(7,6))\n\n```\n\nIn [13]:\n\n```py\npreds,_ = learn.get_preds(ds_type=DatasetType.Test)\n\n```\n\nIn [14]:\n\n```py\ntest_df.has_cactus = preds.numpy()[:, 0]\n\n```\n\nIn [15]:\n\n```py\ntest_df.to_csv('submission.csv', index=False)\n\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/aerial-cactus-identification/xdstudent.md",
    "content": "# XDStudent\n\n> Author: https://www.kaggle.com/xiuchengwang\n\n> From: https://www.kaggle.com/xiuchengwang/xdstudent\n\n> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)\n\n> Score: 0.9999\n\nIn [1]:\n\n```py\n# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.\n\n```\n\n```\n['test', 'sample_submission.csv', 'train.csv', 'train']\n\n```\n\nIn [2]:\n\n```py\nfrom keras.layers import *\nfrom keras.models import Model, Sequential, load_model\nfrom keras import applications\nfrom keras.utils.np_utils import to_categorical\nfrom keras.optimizers import RMSprop, Adam, SGD\nfrom keras.losses import sparse_categorical_crossentropy, binary_crossentropy\nfrom keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\nfrom keras import backend as K\n\n```\n\n```\nUsing TensorFlow backend.\n\n```\n\nIn [3]:\n\n```py\ntrain = pd.read_csv('../input/train.csv')\ntrain.head()\n\n```\n\nOut[3]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |\n| --- | --- | --- |\n| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |\n| --- | --- | --- |\n| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |\n| --- | --- | --- |\n| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |\n| --- | --- | --- |\n| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |\n| --- | --- | --- |\n\nIn [4]:\n\n```py\ntrain['has_cactus'].value_counts()\n\n```\n\nOut[4]:\n\n```\n1    13136\n0     4364\nName: has_cactus, dtype: int64\n```\n\nIn [5]:\n\n```py\nimport matplotlib.pyplot as plt\nimport tqdm\n\nimg = plt.imread('../input/train/train/'+ train['id'][0])\nimg.shape\n\n```\n\nOut[5]:\n\n```\n(32, 32, 3)\n```\n\nIn [6]:\n\n```py\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, roc_auc_score\n\nx_train, x_test, y_train, y_test = train_test_split(train['id'], train['has_cactus'], test_size = 0.1, random_state = 32)\n\n```\n\nIn [7]:\n\n```py\nx_train_arr = []\nfor images in tqdm.tqdm(x_train):\n    img = plt.imread('../input/train/train/' + images)\n    x_train_arr.append(img)\n\nx_train_arr = np.array(x_train_arr)\nprint(x_train_arr.shape)\n\n```\n\n```\n100%|██████████| 15750/15750 [00:18<00:00, 861.12it/s]\n\n```\n\n```\n(15750, 32, 32, 3)\n\n```\n\nIn [8]:\n\n```py\nx_test_arr = []\nfor images in tqdm.tqdm(x_test):\n    img = plt.imread('../input/train/train/' + images)\n    x_test_arr.append(img)\n\nx_test_arr = np.array(x_test_arr)\nprint(x_test_arr.shape)\n\n```\n\n```\n100%|██████████| 1750/1750 [00:02<00:00, 759.34it/s]\n```\n\n```\n(1750, 32, 32, 3)\n\n```\n\nIn [9]:\n\n```py\nx_train_arr = x_train_arr.astype('float32')\nx_test_arr = x_test_arr.astype('float32')\nx_train_arr = x_train_arr/255\nx_test_arr = x_test_arr/255\n\n```\n\nIn [10]:\n\n```py\nfrom keras.applications.densenet import DenseNet201\nfrom keras.layers import *\n\ninputs = Input((32, 32, 3))\nbase_model = DenseNet201(include_top=False, input_shape=(32, 32, 3))#, weights=None\nx = base_model(inputs)\nout1 = GlobalMaxPooling2D()(x)\nout2 = GlobalAveragePooling2D()(x)\nout3 = Flatten()(x)\nout = Concatenate(axis=-1)([out1, out2, out3])\nout = Dropout(0.5)(out)\nout = Dense(256, name=\"3_\")(out)\nout = BatchNormalization()(out)\nout = Activation(\"relu\")(out)\nout = Dense(1, activation=\"sigmoid\", name=\"3_2\")(out)\nmodel = Model(inputs, out)\nmodel.summary()\n\n```\n\n```\nDownloading data from https://github.com/keras-team/keras-applications/releases/download/densenet/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5\n74842112/74836368 [==============================] - 2s 0us/step\n__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\ninput_1 (InputLayer)            (None, 32, 32, 3)    0                                            \n__________________________________________________________________________________________________\ndensenet201 (Model)             (None, 1, 1, 1920)   18321984    input_1[0][0]                    \n__________________________________________________________________________________________________\nglobal_max_pooling2d_1 (GlobalM (None, 1920)         0           densenet201[1][0]                \n__________________________________________________________________________________________________\nglobal_average_pooling2d_1 (Glo (None, 1920)         0           densenet201[1][0]                \n__________________________________________________________________________________________________\nflatten_1 (Flatten)             (None, 1920)         0           densenet201[1][0]                \n__________________________________________________________________________________________________\nconcatenate_1 (Concatenate)     (None, 5760)         0           global_max_pooling2d_1[0][0]     \n                                                                 global_average_pooling2d_1[0][0] \n                                                                 flatten_1[0][0]                  \n__________________________________________________________________________________________________\ndropout_1 (Dropout)             (None, 5760)         0           concatenate_1[0][0]              \n__________________________________________________________________________________________________\n3_ (Dense)                      (None, 256)          1474816     dropout_1[0][0]                  \n__________________________________________________________________________________________________\nbatch_normalization_1 (BatchNor (None, 256)          1024        3_[0][0]                         \n__________________________________________________________________________________________________\nactivation_1 (Activation)       (None, 256)          0           batch_normalization_1[0][0]      \n__________________________________________________________________________________________________\n3_2 (Dense)                     (None, 1)            257         activation_1[0][0]               \n==================================================================================================\nTotal params: 19,798,081\nTrainable params: 19,568,513\nNon-trainable params: 229,568\n__________________________________________________________________________________________________\n\n```\n\nIn [11]:\n\n```py\nbase_model.Trainable=True\n\nset_trainable=False\nfor layer in base_model.layers:\n    layer.trainable = True\n\n```\n\nIn [12]:\n\n```py\nmodel.compile('rmsprop', loss = \"binary_crossentropy\", metrics=[\"accuracy\"])\n\n```\n\nIn [13]:\n\n```py\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import ModelCheckpoint\n\nbatch_size = 128\nepochs = 36\n\nfilepath=\"weights_resnet.hdf5\"\n\ncheckpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\nlearning_rate_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.6, patience=4, verbose=1, mode='max', min_delta=0.0, cooldown=0, min_lr=0)\nearly_stop = EarlyStopping(monitor='val_acc', min_delta=0.0001, patience=25, verbose=1, mode='max', baseline=None, restore_best_weights=True)\n\ntrain_datagen = ImageDataGenerator(\n    rotation_range=40,\n    zoom_range=0.1,\n    vertical_flip=True,\n    horizontal_flip=True)\n\ntrain_datagen.fit(x_train_arr)\nhistory = model.fit_generator(\n    train_datagen.flow(x_train_arr, y_train, batch_size=batch_size),\n    steps_per_epoch=x_train.shape[0] // batch_size,\n    epochs=epochs,\n    validation_data=(x_test_arr, y_test),\n    callbacks=[learning_rate_reduce, checkpoint] \n)\n\n```\n\n```\nEpoch 1/36\n123/123 [==============================] - 75s 611ms/step - loss: 0.0981 - acc: 0.9632 - val_loss: 0.5698 - val_acc: 0.8554\n\nEpoch 00001: val_acc improved from -inf to 0.85543, saving model to weights_resnet.hdf5\nEpoch 2/36\n123/123 [==============================] - 26s 211ms/step - loss: 0.0569 - acc: 0.9842 - val_loss: 2.3376 - val_acc: 0.7554\n\nEpoch 00002: val_acc did not improve from 0.85543\nEpoch 3/36\n123/123 [==============================] - 25s 204ms/step - loss: 0.0379 - acc: 0.9883 - val_loss: 1.3842 - val_acc: 0.7783\n\nEpoch 00003: val_acc did not improve from 0.85543\nEpoch 4/36\n123/123 [==============================] - 25s 203ms/step - loss: 0.0445 - acc: 0.9864 - val_loss: 0.1151 - val_acc: 0.9686\n\nEpoch 00004: val_acc improved from 0.85543 to 0.96857, saving model to weights_resnet.hdf5\nEpoch 5/36\n123/123 [==============================] - 26s 215ms/step - loss: 0.0437 - acc: 0.9873 - val_loss: 0.2282 - val_acc: 0.9726\n\nEpoch 00005: val_acc improved from 0.96857 to 0.97257, saving model to weights_resnet.hdf5\nEpoch 6/36\n123/123 [==============================] - 26s 208ms/step - loss: 0.0288 - acc: 0.9900 - val_loss: 0.3356 - val_acc: 0.9149\n\nEpoch 00006: ReduceLROnPlateau reducing learning rate to 0.0006000000284984708.\n\nEpoch 00006: val_acc did not improve from 0.97257\nEpoch 7/36\n123/123 [==============================] - 26s 209ms/step - loss: 0.0160 - acc: 0.9945 - val_loss: 0.0146 - val_acc: 0.9954\n\nEpoch 00007: val_acc improved from 0.97257 to 0.99543, saving model to weights_resnet.hdf5\nEpoch 8/36\n123/123 [==============================] - 25s 207ms/step - loss: 0.0143 - acc: 0.9954 - val_loss: 0.0158 - val_acc: 0.9937\n\nEpoch 00008: val_acc did not improve from 0.99543\nEpoch 9/36\n123/123 [==============================] - 25s 200ms/step - loss: 0.0254 - acc: 0.9949 - val_loss: 0.0097 - val_acc: 0.9977\n\nEpoch 00009: val_acc improved from 0.99543 to 0.99771, saving model to weights_resnet.hdf5\nEpoch 10/36\n123/123 [==============================] - 25s 207ms/step - loss: 0.0120 - acc: 0.9959 - val_loss: 0.1715 - val_acc: 0.9514\n\nEpoch 00010: ReduceLROnPlateau reducing learning rate to 0.0003600000170990825.\n\nEpoch 00010: val_acc did not improve from 0.99771\nEpoch 11/36\n123/123 [==============================] - 26s 215ms/step - loss: 0.0082 - acc: 0.9975 - val_loss: 0.0119 - val_acc: 0.9966\n\nEpoch 00011: val_acc did not improve from 0.99771\nEpoch 12/36\n123/123 [==============================] - 25s 206ms/step - loss: 0.0092 - acc: 0.9968 - val_loss: 0.4189 - val_acc: 0.9029\n\nEpoch 00012: val_acc did not improve from 0.99771\nEpoch 13/36\n123/123 [==============================] - 25s 205ms/step - loss: 0.0072 - acc: 0.9978 - val_loss: 0.0366 - val_acc: 0.9874\n\nEpoch 00013: val_acc did not improve from 0.99771\nEpoch 14/36\n123/123 [==============================] - 27s 219ms/step - loss: 0.0077 - acc: 0.9973 - val_loss: 0.0312 - val_acc: 0.9920\n\nEpoch 00014: ReduceLROnPlateau reducing learning rate to 0.00021600000327453016.\n\nEpoch 00014: val_acc did not improve from 0.99771\nEpoch 15/36\n123/123 [==============================] - 26s 212ms/step - loss: 0.0048 - acc: 0.9986 - val_loss: 0.0163 - val_acc: 0.9960\n\nEpoch 00015: val_acc did not improve from 0.99771\nEpoch 16/36\n123/123 [==============================] - 25s 203ms/step - loss: 0.0047 - acc: 0.9986 - val_loss: 0.0555 - val_acc: 0.9806\n\nEpoch 00016: val_acc did not improve from 0.99771\nEpoch 17/36\n123/123 [==============================] - 26s 211ms/step - loss: 0.0060 - acc: 0.9982 - val_loss: 0.0492 - val_acc: 0.9943\n\nEpoch 00017: val_acc did not improve from 0.99771\nEpoch 18/36\n123/123 [==============================] - 24s 198ms/step - loss: 0.0045 - acc: 0.9986 - val_loss: 0.0067 - val_acc: 0.9977\n\nEpoch 00018: ReduceLROnPlateau reducing learning rate to 0.00012960000021848827.\n\nEpoch 00018: val_acc did not improve from 0.99771\nEpoch 19/36\n123/123 [==============================] - 25s 206ms/step - loss: 0.0060 - acc: 0.9973 - val_loss: 0.0070 - val_acc: 0.9971\n\nEpoch 00019: val_acc did not improve from 0.99771\nEpoch 20/36\n123/123 [==============================] - 27s 217ms/step - loss: 0.0048 - acc: 0.9989 - val_loss: 0.0065 - val_acc: 0.9983\n\nEpoch 00020: val_acc improved from 0.99771 to 0.99829, saving model to weights_resnet.hdf5\nEpoch 21/36\n123/123 [==============================] - 25s 203ms/step - loss: 0.0235 - acc: 0.9974 - val_loss: 0.0203 - val_acc: 0.9949\n\nEpoch 00021: val_acc did not improve from 0.99829\nEpoch 22/36\n123/123 [==============================] - 24s 199ms/step - loss: 0.0040 - acc: 0.9990 - val_loss: 0.0149 - val_acc: 0.9966\n\nEpoch 00022: ReduceLROnPlateau reducing learning rate to 7.775999838486313e-05.\n\nEpoch 00022: val_acc did not improve from 0.99829\nEpoch 23/36\n123/123 [==============================] - 26s 211ms/step - loss: 0.0039 - acc: 0.9988 - val_loss: 0.0084 - val_acc: 0.9977\n\nEpoch 00023: val_acc did not improve from 0.99829\nEpoch 24/36\n123/123 [==============================] - 25s 200ms/step - loss: 0.0382 - acc: 0.9951 - val_loss: 0.0088 - val_acc: 0.9983\n\nEpoch 00024: val_acc did not improve from 0.99829\nEpoch 25/36\n123/123 [==============================] - 25s 201ms/step - loss: 0.0035 - acc: 0.9989 - val_loss: 0.0054 - val_acc: 0.9983\n\nEpoch 00025: val_acc did not improve from 0.99829\nEpoch 26/36\n123/123 [==============================] - 26s 209ms/step - loss: 0.0246 - acc: 0.9970 - val_loss: 0.0059 - val_acc: 0.9977\n\nEpoch 00026: ReduceLROnPlateau reducing learning rate to 4.6655999904032795e-05.\n\nEpoch 00026: val_acc did not improve from 0.99829\nEpoch 27/36\n123/123 [==============================] - 26s 210ms/step - loss: 0.0032 - acc: 0.9989 - val_loss: 0.0100 - val_acc: 0.9983\n\nEpoch 00027: val_acc did not improve from 0.99829\nEpoch 28/36\n123/123 [==============================] - 25s 205ms/step - loss: 0.0025 - acc: 0.9993 - val_loss: 0.0063 - val_acc: 0.9971\n\nEpoch 00028: val_acc did not improve from 0.99829\nEpoch 29/36\n123/123 [==============================] - 26s 211ms/step - loss: 0.0222 - acc: 0.9981 - val_loss: 0.0077 - val_acc: 0.9977\n\nEpoch 00029: val_acc did not improve from 0.99829\nEpoch 30/36\n123/123 [==============================] - 26s 212ms/step - loss: 0.0300 - acc: 0.9969 - val_loss: 0.0085 - val_acc: 0.9977\n\nEpoch 00030: ReduceLROnPlateau reducing learning rate to 2.799360081553459e-05.\n\nEpoch 00030: val_acc did not improve from 0.99829\nEpoch 31/36\n123/123 [==============================] - 25s 203ms/step - loss: 0.0233 - acc: 0.9966 - val_loss: 0.0155 - val_acc: 0.9960\n\nEpoch 00031: val_acc did not improve from 0.99829\nEpoch 32/36\n123/123 [==============================] - 26s 212ms/step - loss: 0.0036 - acc: 0.9981 - val_loss: 0.0150 - val_acc: 0.9966\n\nEpoch 00032: val_acc did not improve from 0.99829\nEpoch 33/36\n123/123 [==============================] - 26s 209ms/step - loss: 0.0239 - acc: 0.9969 - val_loss: 0.0077 - val_acc: 0.9983\n\nEpoch 00033: val_acc did not improve from 0.99829\nEpoch 34/36\n123/123 [==============================] - 25s 201ms/step - loss: 0.0158 - acc: 0.9967 - val_loss: 0.0112 - val_acc: 0.9966\n\nEpoch 00034: ReduceLROnPlateau reducing learning rate to 1.6796160707599483e-05.\n\nEpoch 00034: val_acc did not improve from 0.99829\nEpoch 35/36\n123/123 [==============================] - 25s 201ms/step - loss: 9.8774e-04 - acc: 0.9997 - val_loss: 0.0110 - val_acc: 0.9977\n\nEpoch 00035: val_acc did not improve from 0.99829\nEpoch 36/36\n123/123 [==============================] - 26s 211ms/step - loss: 0.0012 - acc: 0.9996 - val_loss: 0.0098 - val_acc: 0.9971\n\nEpoch 00036: val_acc did not improve from 0.99829\n\n```\n\nIn [14]:\n\n```py\ntrain_pred = model.predict(x_train_arr, verbose= 1)\nvalid_pred = model.predict(x_test_arr, verbose= 1)\n\ntrain_acc = roc_auc_score(np.round(train_pred), y_train)\nvalid_acc = roc_auc_score(np.round(valid_pred), y_test)\n\n```\n\n```\n15750/15750 [==============================] - 24s 2ms/step\n1750/1750 [==============================] - 2s 1ms/step\n\n```\n\nIn [15]:\n\n```py\nconfusion_matrix(np.round(valid_pred), y_test)\n\n```\n\nOut[15]:\n\n```\narray([[ 442,    0],\n       [   5, 1303]])\n```\n\nIn [16]:\n\n```py\nsample = pd.read_csv('../input/sample_submission.csv')\n\n```\n\nIn [17]:\n\n```py\ntest = []\nfor images in tqdm.tqdm(sample['id']):\n    img = plt.imread('../input/test/test/' + images)\n    test.append(img)\n\ntest = np.array(test)\n\n```\n\n```\n100%|██████████| 4000/4000 [00:04<00:00, 909.30it/s]\n\n```\n\nIn [18]:\n\n```py\ntest = test/255\ntest_pred = model.predict(test, verbose= 1)\n\n```\n\n```\n4000/4000 [==============================] - 5s 1ms/step\n\n```\n\nIn [19]:\n\n```py\nsample['has_cactus'] = test_pred\nsample.head()\n\n```\n\nOut[19]:\n\n|  | id | has_cactus |\n| --- | --- | --- |\n| 0 | 000940378805c44108d287872b2f04ce.jpg | 9.999971e-01 |\n| --- | --- | --- |\n| 1 | 0017242f54ececa4512b4d7937d1e21e.jpg | 9.999970e-01 |\n| --- | --- | --- |\n| 2 | 001ee6d8564003107853118ab87df407.jpg | 9.930815e-08 |\n| --- | --- | --- |\n| 3 | 002e175c3c1e060769475f52182583d0.jpg | 1.431321e-05 |\n| --- | --- | --- |\n| 4 | 0036e44a7e8f7218e9bc7bf8137e4943.jpg | 9.999958e-01 |\n| --- | --- | --- |\n\nIn [20]:\n\n```py\nsample.to_csv('sub.csv', index= False)\n\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/dogs-vs-cats/README.md",
    "content": "# **猫和狗**\n\n![](/img/competitions/playground/dogs-vs-cats.jpg)\n\n> 注意：[项目规范](/docs/kaggle-quickstart.md)\n\n## 比赛说明\n\n* 在本次比赛中，您将编写一个算法来分类图像是否包含狗或猫。这对人类，狗和猫来说很容易。你的电脑会觉得有点困难。\n\n深蓝在1997年在国际象棋比赛中击败卡斯帕罗夫。\n沃森在2011 年击败了Jeopardy最聪明的琐事。\n你能否在2013年从米登斯那里告诉菲多？\n\n> Asirra数据集\n\nWeb服务通常受到人们解决这个难题的挑战，但这对计算机来说很困难。这样的挑战通常被称为 [CAPTCHA](http://www.captcha.net/)  （完全自动公开的图灵测试来告诉计算机和人类）或HIP（人类交互证明）。HIP用于多种用途，例如减少电子邮件和博客垃圾邮件，防止对网站密码进行暴力攻击。\n\n[Asirra](http://research.microsoft.com/en-us/um/redmond/projects/asirra/)（限制访问的动物物种图像识别）是一项HIP，通过询问用户识别猫和狗的照片而工作。这项任务对于计算机来说很难，但研究表明人们可以快速准确地完成任务。许多人甚至认为这很有趣！以下是Asirra界面的一个例子：\n\nAsirra是独一无二的，因为它与全球最大的网站 [Petfinder.com](http://www.petfinder.com/) 合作，  致力于为无家可归的宠物寻找住所。他们向微软研究院提供了超过三百万张猫和狗的图像，由美国各地数千个动物收容所的人员手动分类。Kaggle很幸运能够提供这些数据的一个子集，用于娱乐和研究。 \n\n> 图像识别攻击\n\n虽然随机猜测是最简单的攻击形式，但各种形式的图像识别可以让攻击者做出比随机更好的猜测。照片数据库（各种各样的背景，角度，姿势，照明等）具有巨大的多样性，难以进行准确的自动分类。在多年前进行的一项非正式调查中，计算机视觉专家认为，如果没有现有技术的重大进展，精度高于60％的分类器将很困难。作为参考，60％分类器将12幅图像HIP的猜测概率从1/4096提高到1/459。\n\n> 最先进的\n\n目前的文献表明机器分类器可以在这项任务上得到80％以上的准确度[1]。因此，Asirra不再被认为是安全的。我们创建了这个比赛，以针对这个问题对最新的计算机视觉和深度学习方法进行基准测试 你能破解CAPTCHA吗？你能改善艺术状态吗？你能在猫狗之间创造持久的和平吗？\n\n好的，我们会解决前者。 \n\n> 致谢\n\n我们感谢微软研究院为此次比赛提供数据。\n\n* Jeremy Elson，John R. Douceur，Jon Howell，Jared Saul，Asirra：在计算机和通信安全（CCS）第14届ACM会议论文集计算机械协会会刊上发表的利用调整手动图像分类的CAPTCHA， 2007年10月\n\n## 参赛成员\n\n* 开源组织: [ApacheCN ~ apachecn.org](http://www.apachecn.org/)\n* 参与人员: [片刻](https://github.com/jiangzhonglian)\n\n## 比赛分析\n\n* 回归问题：价格的问题\n* 常用算法： `回归`、`树回归`、`GBDT`、`xgboost`、`lightGBM`\n\n```\n步骤:\n一. 数据分析\n1. 下载并加载数据\n2. 总体预览:了解每列数据的含义,数据的格式等\n3. 数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备\n\n二. 特征工程\n1.根据业务,常识,以及第二步的数据分析构造特征工程.\n2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)\n\n三. 模型选择\n1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.\n2.比较各个模型的分数,然后取效果较好的模型作为基础模型.\n\n四. 模型融合\n\n五. 修改特征和模型参数\n1.可以通过添加或者修改特征,提高模型的上限.\n2.通过修改模型的参数,是模型逼近上限\n\n六. 提交格式\n'''(label: 1=dog, 0=cat)\nid,label\n1,0\n2,0\n3,0\netc...\n'''\n```\n\n## 一. 数据分析\n\n### 数据下载和加载\n\n> 数据获取\n\n* 数据集下载地址：<https://www.kaggle.com/c/dogs-vs-cats/data>\n\n> 特征说明\n\n* Dogs vs. Cats是一个传统的二分类问题。\n    * 训练集包含25000张图片，命名格式为<category>.<num>.jpg, 如cat.10000.jpg、dog.100.jpg\n    * 测试集包含12500张图片，命名为<num>.jpg，如1000.jpg。\n* 参赛者需根据训练集的图片训练模型，并在测试集上进行预测，输出它是狗的概率。\n* 最后提交的csv文件如下，第一列是图片的<num>，第二列是图片为狗的概率。\n\n"
  },
  {
    "path": "docs/Kaggle/competitions/playground/dogs-vs-cats/kernel.md",
    "content": "# Dogs vs. Cats (kaggle 猫狗大战)\n\nCreate an algorithm to distinguish dogs from cats.\n\n正如上面这句话所说，我们的目的就是创建一个算法，来对混合猫狗图片的数据集中，将猫和狗分别识别出来。\n\n## 一、简介\n\n猫狗大战这个项目其实与我们之前做的数字识别类似，只不过是图片复杂了一些。当然，我们这次使用深度学习，来完成我们想要做的事情。\n\n## 二、安装包/第三方库要求\n\n - Python 3.x\n - Pytorch\n\n## 三、数据预处理\n\n### 1、本次的数据来自 kaggle 的比赛项目 [dogs-vs-cats](https://www.kaggle.com/c/dogs-vs-cats)\n\n### 2、查看数据格式\n\n - 训练数据\n\n![trainDataset](/img/competitions/playground/train.png)\n\n - 训练数据集 - 说明：训练数据集中的数据，是经过人工标记的数据，类别和数字之间使用的 \".\" （点）做的分隔。\n\n - 测试数据\n\n![testDataset](/img/competitions/playground/test.png)\n\n - 测试数据集 - 说明：测试数据集中的数据，是没有经过人工标记的数据，没有对应的类别，只有一些相应的数字号码。\n\n### 3、对数据的预处理\n\n#### 3.1、提取训练 & 测试数据集的编号\n\n训练数据集 & 测试数据集 给出的序号和 label 都是在文件名称中。\n\n```python\nimgs = [os.path.join(root, img) for img in os.listdir(root)]\n\n        # test1：即测试数据集， D:/dataset/dogs-vs-cats/test1\n        # train: 即训练数据集，D:/dataset/dogs-vs-cats/train\n        if self.test:\n            # 提取 测试数据集的序号，\n            # 如 x = 'd:/path/123.jpg'，\n            # x.split('.') 得到 ['d:/path/123', 'jpg'] \n            # x.split('.')[-2] 得到 d:/path/123\n            # x.split('.')[-2].split('/') 得到 ['d:', 'path', '123']\n            # x.split('.')[-2].split('/')[-1] 得到 123\n            imgs = sorted(imgs, key=lambda x: int(x.split('.')[-2].split('/')[-1]))\n        else:\n            # 如果不是测试集的话，就是训练集，我们只切分一下，仍然得到序号，123\n            imgs = sorted(imgs, key=lambda x: int(x.split('.')[-2]))\n```\n\n#### 3.2、划分训练集 & 验证集\n\n首先我们知道我们手里的数据现在只有训练集和测试集，并没有验证集。那么为了我们训练得到的模型更好地拟合我们的测试数据，我们人为地将训练数据划分为 训练数据 + 验证数据（比例设置为 7:3）\n\n```python\n# 获取图片的数量\n        imgs_num = len(imgs)\n\n        # 划分训练、验证集，验证集:训练集 = 3:7\n        # 判断是否为测试集\n        if self.test:\n            # 如果是 测试集，那么 就直接赋值\n            self.imgs = imgs\n        # 判断是否为 训练集\n        elif train:\n            # 如果是训练集，那么就把数据集的开始位置的数据 到 70% 部分的数据作为训练集\n            self.imgs = imgs[:int(0.7 * imgs_num)]\n        else:\n            # 这种情况就是划分验证集啦,从 70% 部分的数据 到达数据的末尾，全部作为验证集\n            self.imgs = imgs[int(0.7 * imgs_num):]\n```\n\n#### 3.3、测试集，验证集和训练集的数据转换\n\n```python\n        # 数据的转换操作，测试集，验证集，和训练集的数据转换有所区别\n        if transforms is None:\n            # 如果转换操作没有设置，那我们设置一个转换 \n            normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n            # 测试集 和 验证集 的转换\n            # 判断如果是测试集或者不是训练集（也就是说是验证集），就应用我们下边的转换\n            if self.test or not train:\n                self.trainsforms = T.Compose([T.Resize(224), T.CenterCrop(224), T.ToTensor(), normalize])\n            else:\n                # 如果是训练集的话，使用另外的转换\n                self.transforms = T.Compose([T.Resize(256), T.RandomResizedCrop(224), T.RandomHorizontalFlip(), T.ToTensor(), normalize])\n```\n\n#### 3.4、重写子类 / 函数\n\n这里我们使用了 torch.utils.data 中的一些函数，比如 Dataset\n\nclass torch.utils.data.Dataset 表示 Dataset 的抽象类，所有其他的数据集都应继承该类。所有子类都应该重写 __len__ ，提供数据集大小的方法，和 __getitem__ ，支持从 0 到 len(self) 整数索引的方法。\n\n```python\n    def __len__(self):\n        return len(self.imgs)\n\n    def __getitem__(self, index):\n        img_path = self.imgs[index]\n        # 判断，如果是测试集的数据的话，那就返回对应的序号，比如 d:path/123.jpg 返回 123\n        if self.test:\n            label = int(self.imgs[index].split('.')[-2].split('/')[-1])\n        else:\n            # 如果不是测试集的数据，那么会有相应的类别（label），也就是对应的dog 和 cat，dog 为 1，cat 为0\n            label = 1 if 'dog' in img_path.split('/')[-1] else 0\n        # 这里使用 Pillow 模块，使用 Image 打开一个图片\n        data = Image.open(img_path)\n        # 使用我们定义的 transforms ，将图片转换，详情参考：https://pytorch.org/docs/stable/torchvision/transforms.html#transforms-on-pil-image\n        # 默认的 transforms 设置的是 none\n        data = self.transforms(data)\n        # 将转换完成的 data 以及对应的 label（如果有的话），返回\n        return data,label\n```\n\n#### 3.5、数据加载\n\n```python\n# 训练数据集的路径\ntrain_path = 'D:/dataset/dogs-vs-cats/train'\n# 从训练数据集的存储路径中提取训练数据集\ntrain_dataset = GetData(train_path, train=True)\n# 将训练数据转换成 mini-batch 形式\nload_train = data.DataLoader(train_dataset, batch_size=20, shuffle=True, num_workers=1)\n\n# 测试数据的获取\n# 首先设置测试数据的路径\ntest_path = 'D:/dataset/dogs-vs-cats/test1'\n# 从测试数据集的存储路径中提取测试数据集\ntest_path = GetData(test_path, test=True)\n# 将测试数据转换成 mini-batch 形式\nloader_test = data.DataLoader(test_dataset, batch_size=3, shuffle=True, num_workers=1)\n```\n\n## 四、构建 CNN 模型\n\n```python\n# 调用已经写好的 AlexNet() 模型\ncnn = AlexNet()\n# 将模型打印出来观察一下\nprint(cnn)\n```\n\n## 五、设置相应的优化器和损失函数\n\n我们已经构造完成了 CNN 模型，并将我们所需要的数据进行了相应的预处理。那我们接下来的一步就是定义相应的损失函数和优化函数。\n\ntorch.optim 是一个实现各种优化算法的软件包。\n\n```python\n# 设置优化器和损失函数\n# 这里我们使用 Adam 优化器，使用的损失函数是 交叉熵损失\noptimizer = torch.optim.Adam(cnn.parameters(), lr=0.005, betas=(0.9, 0.99))  # 优化所有的 cnn 参数\nloss_func = nn.CrossEntropyLoss()  # 目标 label 不是 one-hotted 类型的\n```\n\n## 六、训练模型\n\n数据以及相对应的损失函数和优化器，我们都已经设置好了，那接下来就是紧张刺激的训练模型环节了。\n\n```python\n# 训练模型\n# 设置训练模型的次数，这里我们设置的是 10 次，也就是用我们的训练数据集对我们的模型训练 10 次，为了节省时间，我们可以只训练 1 次\nEPOCH = 10\n# 训练和测试\nfor epoch in range(EPOCH):\n        num = 0\n        # 给出 batch 数据，在迭代 train_loader 的时候对 x 进行 normalize\n        for step, (x, y) in enumerate(loader_train):\n            b_x = Variable(x)  # batch x\n            b_y = Variable(y)  # batch y\n\n            output = cnn(b_x)  # cnn 的输出\n            loss = loss_func(output, b_y)  # 交叉熵损失\n            optimizer.zero_grad()  # 在这一步的训练步骤上，进行梯度清零\n            loss.backward()  # 反向传播，并进行计算梯度\n            optimizer.step()  # 应用梯度\n\n            # 可以打印一下\n            # print('-'*30, step)\n            if step % 20 == 0:\n                num += 1\n                for _, (x_t, y_test) in enumerate(loader_test):\n                    x_test = Variable(x_t)  # batch x\n                    test_output = cnn(x_test)\n                    pred_y = torch.max(test_output, 1)[1].data.squeeze()\n                    accuracy = sum(pred_y == y_test) / float(y_test.size(0))\n                    print('Epoch: ', epoch, '| Num: ',  num, '| Step: ',  step, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.4f' % accuracy)\n```"
  },
  {
    "path": "docs/Kaggle/competitions/playground/leaf-classification/leaf-classification-competition-1st-place-winners-interview-ivan-sosnovik.md",
    "content": "# 树叶分类竞赛：Ivan Sosnovik 的冠军采访\n\n> 原文：[Leaf Classification Competition: 1st Place Winner's Interview, Ivan Sosnovik](http://blog.kaggle.com/2017/03/24/leaf-classification-competition-1st-place-winners-interview-ivan-sosnovik/)\n> \n> 译者：[飞龙](https://github.com/wizardforcel)\n> \n> 自豪地采用[谷歌翻译](https://translate.google.cn)\n\n你能看到随机森林的树叶吗？[树叶分类入门竞赛](https://www.kaggle.com/c/leaf-classification)于 2016 年 8 月至 2017 年 2 月在 Kaggle 上进行。Kagglers 面临着根据图像和预先提取的特征正确识别 99 种树叶的挑战。在这位获胜者的采访中，Kaggler [Ivan Sosnovik](https://www.kaggle.com/isosnovik) 分享了他的冠军方法。他解释了在这个特征工程竞赛中，他使用逻辑回归和随机森林算法比 XGBoost 或卷积神经网络更好运。\n\n## 简介\n\n我是莫斯科 Skoltech 的数据分析硕士生。 大约一年前，当我参加大学的第一门 ML 课程时，我加入了 Kaggle。 第一场比赛是 [What's Cooking](https://www.kaggle.com/c/whats-cooking)。 从那以后，我参加了几场 Kaggle 比赛，但没有那么多关注它。 理解 ML 方法的工作方式更像是一种练习。\n\n![](https://i2.wp.com/blog.kaggle.com/wp-content/uploads/2017/03/Screen-Shot-2017-03-23-at-2.31.54-PM.png?resize=1184%2C311)\n\n树叶分类的想法非常简单和具有挑战性。 看起来像我不需要堆叠这么多模型，解决方案可能是优雅的。 此外，数据总量仅为 100 多 MB，即使使用笔记本电脑也可以进行学习。 这是非常有希望的，因为大多数计算应该在我的 MacBook Air 上使用 1.3 GHz Intel Core i5 和 4GB RAM 完成。\n\n之前我曾经处理过黑白图像。 我家附近有一片森林。 但是，在这次比赛中，它没有给我这么多的好处。\n\n## 让我们看看技术\n\n当我参加比赛时，发布了几个排名前20％的内核。 解决方案使用最初提取的特征和 Logistic 回归。 它的 logloss  约为 0.03818。通过调整参数，无法实现显着的改进。为了提高质量，必须进行特征工程。似乎没有人这样做，因为顶级解决方案的得分略高于我的。\n\n## 特征工程\n\n我先做了第一件事，并绘制了每个类的图像。\n\n![](https://i2.wp.com/blog.kaggle.com/wp-content/uploads/2017/03/all_leaves.png?resize=1184%2C770)\n\n原始图像具有不同的分辨率，旋转，纵横比，宽度和高度。 但是，类中每个参数的变化小于类之间的变化。 因此，可以即时构建一些信息特征。 他们是：\n\n+   宽和高\n+   纵横比：`width / height`\n+   面积：`width * height`\n+   是否横向：`int(width > height)`\n\n另一个看似有用的非常有用的特征是图像像素的平均值。\n\n我将这些特征添加到已经提取的特征中。 Logistic 回归改善了结果。 但是，大部分工作尚未完成。\n\n所有上述特征都不代表图像的内容。\n\n## PCA\n\n尽管神经网络作为特征提取器取得了成功，但我仍然喜欢 PCA。它很简单，允许人们在`IR^N`中获得图像的有用表示。 首先，将图像重新调整为`50x50`。然后应用 PCA。 将成分添加到先前提取的特征集中。\n\n![](https://i0.wp.com/blog.kaggle.com/wp-content/uploads/2017/03/eigenvalue.png?w=872)\n\n成分数量各不相同。 最后，我使用了`N = 35`个主成分。 这种方法表明 logloss 约为 0.01511。\n\n## Moments 和 hull\n\n为了生成更多特征，我使用了 OpenCV。这里是如何获取图像的 Moments 和 hull 的精彩[教程](http://docs.opencv.org/3.1.0/dd/d49/tutorial_py_contour_features.html)。 我还添加了几个特征的一些成对乘法。\n\n最后的特征如下：\n\n+   初始特征\n+   宽高，比例，以及其他\n+   PCA\n+   Moments\n\nLogistic 回归表明 loglos 约为 0.00686。\n\n## 核心思想\n\n所有上述都证明了良好的结果。 这样的结果适合于现实生活中的应用。 但是，它可以得到加强。\n\n### 不确定性\n\n大多数对象都有特定的决策：只有一个类别的 p 约为 1.0的类，其余的 p 小于 0.01。 但是，我在预测中发现了几个具有不确定性的对象，如下所示：\n\n![](https://i1.wp.com/blog.kaggle.com/wp-content/uploads/2017/03/uncertain_case.png?w=564)\n\n混淆类别的集合很小（15 个类别分成几个小组），所以我决定查看树叶的图片，并检查我是否可以对它们进行分类。 结果如下：\n\n![](https://i1.wp.com/blog.kaggle.com/wp-content/uploads/2017/03/68_86.png?resize=1184%2C203)\n\n![](https://i2.wp.com/blog.kaggle.com/wp-content/uploads/2017/03/22_24_29.png?resize=1184%2C298)\n\n我必须承认，对于不同的亚种，Quercus'（橡树）的树叶看起来几乎相同。 我想，我可以区分桉树（Eucalyptus ）和山茱萸（Cornus），但亚种的分类对我来说似乎很复杂。\n\n\n## 你能看到随机森林的树叶吗\n\n我的解决方案的关键思想是创建另一个分类器，它将仅针对混淆类进行预测。 我试过的第一个是来自`sklearn`的`RandomForestClassifier`，它在调整超参数后得到了很好的结果。 随机森林使用逻辑回归的相同数据进行训练，但仅使用来自混淆类的对象。\n\n如果逻辑回归给出了对象的不确定预测，则使用随机森林分类器的预测。 随机森林给出了 15 个类的概率，其余假设为绝对0。\n\n最后的流水线如下：\n\n![](https://i1.wp.com/blog.kaggle.com/wp-content/uploads/2017/03/pipeline.png?w=985)\n\n### 阈值\n\n排行榜得分是在整个数据集上计算的。 这就是为什么一些有风险的方法可以用于这场比赛。\n\n提交由多分类 logloss 评估。\n\n![](http://s0.wp.com/latex.php?zoom=1.5625&latex=+logloss+%3D+-+%5Cfrac%7B1%7D%7BN%7D+%5Csum_%7Bi%3D1%7D%5E%7BN%7D+%5Csum_%7Bj%3D1%7D%5E%7BM%7D+y_%7Bij%7D%5Clog%28p_%7Bij%7D%29%3Cbr+%2F%3E+&bg=ffffff&fg=000&s=0)\n\n其中 N，M 是对象和类的数量，`p_{ij}`是预测，`y_ {ij}`是指标：如果对象`i`在类`j`中，则`y_ {ij} = 1`，否则它等于 0。 如果模型正确地选择了类，以下方法将减少整体 logloss，否则它将显着增加。\n\n在阈值处理后，我得到了 logloss = 0.0 的分数。就是这样。所有标签都是正确的。\n\n## 接下来\n\n我已经尝试了几种方法，它们显示适当结果但未在最终流水线中使用。 此外，我有了一些想法，对于如何使解决方案更优雅。 在本节中，我将尝试讨论它们。\n\n### XGBoost\n\ndmlc 的 [XGBoost](https://github.com/dmlc/xgboost) 是一个很棒的工具。 我之前在几个比赛中使用过它，并决定在最初提取的特征上训练它。 它表现出与逻辑回归相同的分数甚至更差，但时间消耗更大。\n\n### 提交的 blending\n\n在我想出随机森林用作第二个分类器的想法之前，我尝试了不同的单模型方法。 因此我收集了很多提交。 一个微小的想法是将提交混合：使用预测的平均值或加权平均值。 结果也不是很好。\n\n### 神经网络\n\n神经网络是我试图实现的第一个想法之一。 卷积神经网络是很好的特征提取器，因此，它们可以用作第一级模型，甚至可以用作主分类器。 原始图像具有不同的分辨率。 我将它们重新调整为`50x50`。在我的笔记本电脑上进行 CNN 训练太费时间，而无法在合理的时间内选择合适的架构，所以经过几个小时的训练后我拒绝了这个想法。 我相信，CNN 可以为这个数据集提供准确的预测。\n"
  },
  {
    "path": "docs/Kaggle/kernel.md",
    "content": "# Kaggle Kernel 备份\n\n+   [第一部分](https://github.com/apachecn/kaggle-kernel-pt1)\n+   [第二部分](https://github.com/apachecn/kaggle-kernel-pt2)\n+   [第三部分](https://github.com/apachecn/kaggle-kernel-pt3)\n+   [第四部分](https://github.com/apachecn/kaggle-kernel-pt4)"
  },
  {
    "path": "docs/Kaggle/learn/embeddings/README.md",
    "content": "# Kaggle 官方教程：嵌入\n\n> 原文：[Embeddings](https://www.kaggle.com/learn/embeddings)\n> \n> 译者：[飞龙](https://github.com/wizardforcel)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nP.S...\n\n本课程仍处于测试阶段，因此我很乐意收到你的反馈意见。 如果你有时间填写[本课程的超短期调查](https://form.jotform.com/82826168584267)，我将非常感激。 你也可以在下面的评论中或在[学习论坛](https://www.kaggle.com/learn-forum)上留下公开反馈。\n\n## 一、嵌入层\n\n欢迎阅读嵌入主题的第一课。 在本课程中，我将展示如何使用`tf.keras` API 实现带嵌入层的模型。 嵌入是一种技术，使深度神经网络能够处理稀疏的类别变量。\n\n### 稀疏类别变量\n\n我的意思是一个具有许多可能值（高基数）的类别变量，其中少数（通常只有 1）存在于任何给定的观察中。 一个很好的例子是词汇。 英语中的词汇是成千上万的，但一条推文可能只有十几个词。 词嵌入是将深度学习应用于自然语言的关键技术。 但其他例子还有很多。\n\n例如，[洛杉矶餐馆检查](https://www.kaggle.com/meganrisdal/la-county-restaurant-inspections-and-violations)的这个数据集有几个稀疏的分类变量，包括：\n\n+   `employee_id`：卫生部门的哪些员工进行了这次检查？ （约 250 个不同的值）\n+   `facility_zip`：餐厅的邮政编码是什么？ （约 3,000 个不同的值）\n+   `owner_name`：谁拥有餐厅？ （约 35,000 个不同的值）\n\n对于将任何这些变量用作网络的输入，嵌入层是个好主意。\n\n在本课程中，我将使用 [MovieLens 数据集](https://www.kaggle.com/grouplens/movielens-20m-dataset)作为示例。\n\n### MovieLens\n\nMovieLens 数据集由用户给电影的评分组成。这是一个示例：\n\n\n| | userId | movieId | rating | y | title | year |\n| --- | --- | --- | --- | --- | --- | --- |\n| 12904240 | 85731 | 1883 | 4.5 | 0.974498 | Labyrinth | 1986 |\n| 6089380 | 45008 | 1221 | 4.5 | 0.974498 | Femme Nikita, La (Nikita) | 1990 |\n| 17901393 | 125144 | 3948 | 4.0 | 0.474498 | The Alamo | 1960 |\n| 9024816 | 122230 | 3027 | 3.5 | -0.025502 | Toy Story 2 | 1999 |\n| 11655659 | 21156 | 5202 | 3.0 | -0.525502 | My Big Fat Greek Wedding |\n\n评分范围是 0.5 到 5。我们的目标是预测由给定用户`ui`给出的，特定电影`mj`的评分。 （列`y`只是评分列的副本，减去了平均值 - 这在以后会有用。）\n\n`userId`和`movieId`都是稀疏分类变量。 它们有许多可能的值：\n\n```\n138,493 个独立用户评分了 26,744 个不同的电影（总评分是 20,000,263 个）\n```\n\n### 在 Keras 中创建评分预测模型\n\n我们想要构建一个模型，它接受用户`ui`和电影`mj`，并输出 0.5-5 的数字，表示我们认为该用户将为该电影评分多少星。\n\n> 注：你可能已经注意到`MovieLens`数据集包含每部电影的信息，例如标题，发行年份，一组流派和用户指定的标签。 但就目前而言，我们不会试图利用任何额外的信息。\n\n我说我们需要一个嵌入层来处理这些输入。 为什么？ 让我们回顾一些替代方案，看看为什么它们不起作用。\n\n### 坏主意 #1：使用用户和电影 ID 作为数值输入\n\n为什么不将用户 ID 和电影 ID 作为数值来输入，然后添加一些密集层？即：\n\n```py\nmodel = keras.Sequential([\n    # 2 个输入值：用户 ID 和电影 ID\n    keras.layers.Dense(256, input_dim=2, activation='relu'),\n    keras.layers.Dense(32, activation='relu'),\n    # 单个输出节点，包含预测的评分\n    keras.layers.Dense(1)\n])\n```\n\n用最简单的术语来说，神经网络的原理是对输入进行数学运算。 但分配给用户和电影的 ID 的实际数值是没有意义的。《辛德勒的名单》的 id 为 527，而《非常嫌疑犯》的 id 为 50，但这并不意味着《辛德勒的名单》比《非常嫌疑犯》大十倍。\n\n### 坏主意 #2：独热编码的用户和电影输入\n\n如果你不熟悉单热编码，可能需要查看我们的课程[“使用独热编码处理类别数据”](https://www.kaggle.com/dansbecker/using-categorical-data-with-one-hot-encoding)。\n\n在该课程中，我们说独热编码是“类别数据的标准方法”。 那么为什么这是一个坏主意呢？ 让我们看看模型是什么样子，它接受独热编码的用户和电影。\n\n```py\ninput_size = n_movies + n_users\nprint(\"Input size = {:,} ({:,} movies + {:,} users)\".format(\n    input_size, n_movies, n_users,\n))\nmodel = keras.Sequential([\n    # 一个带有 128 个单元的隐藏层\n    keras.layers.Dense(128, input_dim=input_size, activation='relu'),\n    # 单个输出节点，包含预测的评分\n    keras.layers.Dense(1)\n])\nmodel.summary()\n'''\nInput size = 165,237 (26,744 movies + 138,493 users)\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense_3 (Dense)              (None, 128)               21150464  \n_________________________________________________________________\ndense_4 (Dense)              (None, 1)                 129       \n=================================================================\nTotal params: 21,150,593\nTrainable params: 21,150,593\nNon-trainable params: 0\n_________________________________________________________________\n'''\n```\n\n这里的一个基本问题是扩展和效率。 我们模型的单个输入是包含 165,237 个数字的向量（其中我们知道 165,235 是零）。 我们整个 2000 万个评分实例数据集的特征数据，将需要一个大小为 20,000,000 x 165,237 或大约 3 万亿个数字的二维数组。 但愿你能把这一切都放进内存中！\n\n此外，在我们的模型上进行训练和推断将是低效的。 为了计算我们的第一个隐藏层的激活，我们需要将我们的 165k 输入乘以大约 2100 万个权重 - 但是这些乘积的绝大多数都将为零。\n\n对于具有少量可能值的分类变量，例如`{Red, Yellow, Green}`或`{Monday, Tuesday, Wednesday, Friday, Saturday, Sunday}`，独热编码是合适的。 但在像我们的电影推荐问题的情况下，它并不是那么好，其中变量有数十或数十万个可能的值。\n\n### 好主意：嵌入层\n\n简而言之，嵌入层将一组离散对象（如单词，用户或电影）中的每个元素映射到实数的密集（嵌入）向量。\n\n> 注：一个关键的实现细节是嵌入层接受被嵌入实体的索引作为输入（即我们可以将`userId`和`movieId`作为输入）。 你可以将其视为一种“查找表”。 这比采用独热向量并进行巨大的矩阵乘法要有效得多！\n\n例如，如果我们为电影学习大小为 8 的嵌入，则《律政俏佳人》（`index = 4352`）的嵌入可能如下所示：\n\n```\n[1.624,−0.612,−0.528,−1.073,0.865,−2.302,1.745,−0.761]\n```\n\n它们来自哪里？ 我们使用随机噪声为每个用户和电影初始化嵌入，然后我们将它们训练，作为整体评分预测模型训练过程的一部分。\n\n他们的意思是什么？ 如果对象的嵌入有任何好处，它应该捕获该对象的一些有用的潜在属性。 但这里的关键词是潜在，也就是隐藏的。 由模型来发现实体的任何属性，并在嵌入空间中对它们编码，对预测任务有用。 听起来很神秘？ 在后面的课程中，我将展示一些解释习得的嵌入的技术，例如使用 t-SNE 算法将它们可视化。\n\n### 实现它\n\n我希望我的模型是这样：\n\n![](/img/learn/embeddings/emb-1.png)\n\n需要注意的一个关键点是，这个网络不仅仅是从输入到输出的一堆层级。 我们将用户和电影视为单独的输入，只有在每个输入经过自己的嵌入层之后才会聚集在一起。\n\n这意味着[`keras.Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential)类（你可能从我们的[图像数据深度学习课程](https://www.kaggle.com/learn/deep-learning)中熟悉它）将无法工作。 我们需要使用[`keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)类转向更强大的“函数式 API”。 函数式 API 的更多详细信息，请查看 [Keras 的指南](https://keras.io/getting-started/functional-api-guide/)。\n\n这是代码：\n\n```py\nhidden_units = (32,4)\nmovie_embedding_size = 8\nuser_embedding_size = 8\n\n# 每个实例将包含两个输入：单个用户 ID 和单个电影 ID\nuser_id_input = keras.Input(shape=(1,), name='user_id')\nmovie_id_input = keras.Input(shape=(1,), name='movie_id')\nuser_embedded = keras.layers.Embedding(df.userId.max()+1, user_embedding_size, \n                                       input_length=1, name='user_embedding')(user_id_input)\nmovie_embedded = keras.layers.Embedding(df.movieId.max()+1, movie_embedding_size, \n                                        input_length=1, name='movie_embedding')(movie_id_input)\n# 连接嵌入（并删除无用的额外维度）\nconcatenated = keras.layers.Concatenate()([user_embedded, movie_embedded])\nout = keras.layers.Flatten()(concatenated)\n\n# 添加一个或多个隐层\nfor n_hidden in hidden_units:\n    out = keras.layers.Dense(n_hidden, activation='relu')(out)\n\n# 单一输出：我们的预测评分\nout = keras.layers.Dense(1, activation='linear', name='prediction')(out)\n\nmodel = keras.Model(\n    inputs = [user_id_input, movie_id_input],\n    outputs = out,\n)\nmodel.summary(line_length=88)\n'''\n________________________________________________________________________________________\nLayer (type)                 Output Shape       Param #   Connected to                  \n========================================================================================\nuser_id (InputLayer)         (None, 1)          0                                       \n________________________________________________________________________________________\nmovie_id (InputLayer)        (None, 1)          0                                       \n________________________________________________________________________________________\nuser_embedding (Embedding)   (None, 1, 8)       1107952   user_id[0][0]                 \n________________________________________________________________________________________\nmovie_embedding (Embedding)  (None, 1, 8)       213952    movie_id[0][0]                \n________________________________________________________________________________________\nconcatenate (Concatenate)    (None, 1, 16)      0         user_embedding[0][0]          \n                                                          movie_embedding[0][0]         \n________________________________________________________________________________________\nflatten (Flatten)            (None, 16)         0         concatenate[0][0]             \n________________________________________________________________________________________\ndense_5 (Dense)              (None, 32)         544       flatten[0][0]                 \n________________________________________________________________________________________\ndense_6 (Dense)              (None, 4)          132       dense_5[0][0]                 \n________________________________________________________________________________________\nprediction (Dense)           (None, 1)          5         dense_6[0][0]                 \n========================================================================================\nTotal params: 1,322,585\nTrainable params: 1,322,585\nNon-trainable params: 0\n________________________________________________________________________________________\n'''\n```\n\n### 训练\n\n我们将编译我们的模型，来最小化平方误差（'MSE'）。 我们还将绝对值误差（'MAE'）作为在训练期间报告的度量标准，因为它更容易解释。\n\n> 需要考虑的事情：我们知道评分只能取值`{0.5,1,1.5,2,2.5,3,3.5,4,4.5,5}` - 所以为什么不将其视为 10 类的多类分类问题 ，每个可能的星级评分一个？\n\n```py\nmodel.compile(\n    # 技术说明：使用嵌入层时，我强烈建议使用\n    # tf.train 中发现的优化器之一：\n    # https://www.tensorflow.org/api_guides/python/train#Optimizers\n    # 传入像 'adam' 或 'SGD' 这样的字符串，会加载一个 keras 优化器 \n    # （在 tf.keras.optimizers 下寻找）。 对于像这样的问题，它们似乎要慢得多，\n    # 因为它们无法有效处理稀疏梯度更新。\n    tf.train.AdamOptimizer(0.005),\n    loss='MSE',\n    metrics=['MAE'],\n)\n```\n\n让我们训练模型：\n\n> 注：我传入`df.y`而不是`df.rating`，作为我的目标变量。`y`列只是评分的“中心”版本 - 即评分列减去其在训练集上的平均值。 例如，如果训练集中的总体平均评分是 3 星，那么我们将 3 星评分翻译为 0, 5星评分为 2.0 等等，来获得`y`。 这是深度学习中的常见做法，并且往往有助于在更少的时期内获得更好的结果。 对于更多详细信息，请随意使用我在 MovieLens 数据集上执行的所有预处理来检查[这个内核](https://www.kaggle.com/colinmorris/movielens-preprocessing)。\n\n```py\nhistory = model.fit(\n    [df.userId, df.movieId],\n    df.y,\n    batch_size=5000,\n    epochs=20,\n    verbose=0,\n    validation_split=.05,\n);\n```\n\n为了判断我们的模型是否良好，有一个基线是有帮助的。 在下面的单元格中，我们计算了几个虚拟基线的误差：始终预测全局平均评分，以及预测每部电影的平均评分：\n\n+   训练集的平均评分：3.53 星\n+   总是预测全局平均评分，结果为 MAE=0.84，MSE=1.10\n+   预测每部电影的平均评分，结果为 MAE=0.73，MSE=0.88\n\n\n这是我们的嵌入模型的绝对误差随时间的绘图。 为了进行比较，我们的最佳基线（预测每部电影的平均评分）用虚线标出：\n\n![](/img/learn/embeddings/emb-2.png)\n\n与基线相比，我们能够将平均误差降低超过 0.1 星（或约 15%）。不错！\n\n### 示例预测\n\n让我们尝试一些示例预测作为健全性检查。 我们首先从数据集中随机挑选一个特定用户。\n\n```\n用户 #26556 评分了 21 个电影（平均评分为 3.7）\n```\n\n| | userId | movieId | rating | title | year |\n| --- | --- | --- | --- | --- | --- |\n| 4421455 | 26556 | 2705 | 5.0 | Airplane! | 1980 |\n| 14722970 | 26556 | 2706 | 5.0 | Airplane II: The Sequel | 1982 |\n| 7435440 | 26556 | 2286 | 4.5 | Fletch | 1985 |\n| 16621016 | 26556 | 2216 | 4.5 | History of the World: Part I | 1981 |\n| 11648630 | 26556 | 534 | 4.5 | Six Degrees of Separation | 1993 |\n| 14805184 | 26556 | 937 | 4.5 | Mr. Smith Goes to Washington | 1939 |\n| 14313285 | 26556 | 2102 | 4.5 | Strangers on a Train | 1951 |\n| 13671173 | 26556 | 2863 | 4.5 | Dr. No | 1962 |\n| 13661434 | 26556 | 913 | 4.0 | Notorious | 1946 |\n| 11938282 | 26556 | 916 | 4.0 | To Catch a Thief | 1955 |\n| 2354167 | 26556 | 3890 | 4.0 | Diamonds Are Forever | 1971 |\n| 16095891 | 26556 | 730 | 4.0 | Spy Hard | 1996 |\n| 16265128 | 26556 | 3414 | 3.5 | Network | 1976 |\n| 13050537 | 26556 | 1414 | 3.5 | Waiting for Guffman | 1996 |\n| 9891416 | 26556 | 2907 | 3.5 | Thunderball | 1965 |\n| 3496223 | 26556 | 4917 | 3.5 | Gosford Park | 2001 |\n| 1996728 | 26556 | 1861 | 3.0 | On the Waterfront | 1954 |\n| 15893218 | 26556 | 1082 | 2.5 | A Streetcar Named Desire | 1951 |\n| 13875921 | 26556 | 3445 | 2.5 | Keeping the Faith | 2000 |\n| 13163853 | 26556 | 1225 | 2.0 | The Day the Earth Stood Still | 1951 |\n| 7262983 | 26556 | 2348 | 0.5 | A Civil Action | 1998 |\n\n用户 26556 给电影《空前绝后满天飞》和《空前绝后满天飞 II》打了两个完美的评分。很棒的选择！ 也许他们也会喜欢《白头神探》系列 - 另一系列由 Leslie Nielsen 主演的恶搞电影。\n\n我们没有那么多关于这个用户讨厌什么的证据。 我们不根据他们的少数低评分做推断，用户不喜欢什么的更好推断是，他们甚至没有评价的电影类型。 让我们再举几个电影的例子，根据用户的评分历史记录，他们似乎不太可能看过。\n\n```py\ncandidate_movies = movies[\n    movies.title.str.contains('Naked Gun')\n    | (movies.title == 'The Sisterhood of the Traveling Pants')\n    | (movies.title == 'Lilo & Stitch')\n].copy()\n\npreds = model.predict([\n    [uid] * len(candidate_movies), # 用户 ID\n    candidate_movies.index, # 电影 ID\n])\n# 注意：记住我们在 'y' 上训练，这是评分列的中心为 0 的版本。\n# 要将我们模型的输出值转换为 [0.5, 5] 原始的星级评分范围， \n# 我们需要通过添加均值来对值“去中心化”\nrow = df.iloc[0] # rating 和 y 之间的差对于所有行都是相同的，所以我们可以使用第一行\ny_delta = row.rating - row.y\ncandidate_movies['predicted_rating'] = preds + y_delta\n# 添加一列，带有我们的预测评分（对于此用户）\n# 和电影对于数据集中所有用户的总体平均评分之间的差 \ncandidate_movies['delta'] = candidate_movies['predicted_rating'] - candidate_movies['mean_rating']\ncandidate_movies.sort_values(by='delta', ascending=False)\n```\n\n|  | title | year | mean_rating | n_ratings | predicted_rating | delta |\n| --- | --- | --- | --- | --- | --- | --- |\n| movieId |  |  |  |  |  |  |\n| 366 | Naked Gun 33 1/3: The Final Insult | 1994 | 2.954226 | 13534.0 | 3.816926 | 0.862699 |\n| 3776 | The Naked Gun 2 1/2: The Smell of Fear | 1991 | 3.132616 | 4415.0 | 3.946124 | 0.813508 |\n| 3775 | The Naked Gun: From the Files of Police Squad! | 1988 | 3.580381 | 6973.0 | 4.236419 | 0.656037 |\n| 5347 | Lilo & Stitch | 2002 | 3.489323 | 4402.0 | 3.971318 | 0.481995 |\n| 10138 | The Sisterhood of the Traveling Pants | 2005 | 3.369987 | 773.0 | 2.041227 | -1.328760 |\n\n看起来很合理！ 对于《白头神探》系列中的每部电影，我们对此用户的预测评分，大约比数据集中平均评分高一星，而我们的“out of left field”使它们的预测评分低于平均值。\n\n### 你的回合\n\n前往[练习笔记本](https://www.kaggle.com/kernels/fork/1598432)，进行嵌入层的实践练习。\n\n## 二、用于推荐问题的矩阵分解\n\n在上一课中，我们训练了一个模型来预测在 MovieLens 数据集中，用户给电影的评分。 提醒一下，模型看起来像这样：\n\n![](/img/learn/embeddings/emb-1.png)\n\n我们为电影和用户查找嵌入向量，将它们连接在一起。 然后我们添加一些隐层。 最后，这些在一个输出节点汇集在一起来预测评分。\n\n在本节课中，我将展示一个更简单的架构，来解决同样的问题：矩阵分解。 更简单可以是一件非常好的事情！ 有时，简单的模型会快速收敛到适当的解决方案，一个更复杂的模型可能会过拟合或无法收敛。\n\n这是我们的矩阵分解模型的样子：\n\n![](/img/learn/embeddings/emb-3.png)\n\n### 点积\n\n让我们回顾一下数学。 如果你是线性代数专家，请跳过本节。\n\n两个长度为`n`的向量`a`和`b`的点积定义为：\n\n![](/img/learn/embeddings/emb-4.png)\n\n结果是单个标量（不是向量）。\n\n点积仅为相同长度的矢量而定义。 这意味着我们需要为电影嵌入和用户嵌入使用相同的大小。\n\n例如，假设我们已经训练了大小为 4 的嵌入，并且电影 Twister 由向量表示：\n\n```\nm_Twister=[ 1.0 −0.5 0.3 −0.1 ]\n```\n\n用户 Stanley  表示为：\n\n```\nu_Stanley=[ −0.2 1.5 −0.1 0.9 ]\n```\n\n我们认为 Stanley 会给 Twister 什么评分？ 我们可以将模型的输出计算为：\n\n```\nm_Twister · u_Stanley \n= (1.0·−0.2)+(−0.5·1.5)+(0.3·−0.1)+(−0.1·0.9)\n= −1.07\n```\n\n因为我们正在在评分列的中心版本上训练，所以我们的模型输出的比例为 0 等于训练集中的总体平均评分（约 3.5）。 因此我们预测 Stanley 将给 Twister `3.5+(−1.07)=2.43`星。\n\n### 为什么\n\n有一个直观的解释，支持了以这种方式组合我们的嵌入向量的决定。 假设我们的电影嵌入空间的维度对应于以下变化的轴：\n\n维度 1：多么令人激动？\n维度 2：多么浪漫？\n维度 3：目标受众有多成熟？\n维度 4：多么好笑？\n\n因此，Twister 是一部令人激动的灾难电影，`m1`的正值为 1.0。\n\n简单来说，`u1`告诉我们“这个用户对动作片的看法如何？”。 他们喜欢它吗？讨厌它？还是不喜欢也不讨厌？\n\nStanley 的向量告诉，我们他是浪漫和喜剧的忠实粉丝，并且略微不喜欢动作和成熟的内容。 如果我们给他一部类似于最后一部的电影，除了它有很多浪漫元素，会怎么样？\n\n```\nm_Titanic=[ 1.0 1.1 0.3 −0.1 ]\n```\n\n不难预测这会如何影响我们的评分输出。 我们给 Stanley 更多他喜欢的东西，所以他的预测评分会增加。\n\n```\npredicted_rating(Stanley,Titanic)\n= m_Titanic·u_Stanley+3.5\n=(1.0·−0.2)+(1.1·1.5)+(0.3·−0.1)+(−0.1·0.9)+3.5\n=4.83 stars\n```\n\n> 注：在实践中，我们的电影嵌入的维度的含义不会那么明确，但我们的电影嵌入空间和用户嵌入空间的含义从根本上联系在一起，这仍然是正确的：`ui`总是代表“这个用户多么喜欢某个电影，其质量由`mi`代表？“ （希望这也提供了一些直觉，为什么电影嵌入空间和用户嵌入空间在这个技巧中必须大小相同。）\n\n### 实现它\n\n创建此模型的代码，类似于我们在上一课中编写的代码，除了我使用点积层来组合用户和电影嵌入层的输出（而不是连接它们，并输入到密集层）。\n\n```py\nmovie_embedding_size = user_embedding_size = 8\n\n# 每个实例由两个输入组成：单个用户 ID 和单个电影 ID\nuser_id_input = keras.Input(shape=(1,), name='user_id')\nmovie_id_input = keras.Input(shape=(1,), name='movie_id')\nuser_embedded = keras.layers.Embedding(df.userId.max()+1, user_embedding_size, \n                                       input_length=1, name='user_embedding')(user_id_input)\nmovie_embedded = keras.layers.Embedding(df.movieId.max()+1, movie_embedding_size, \n                                        input_length=1, name='movie_embedding')(movie_id_input)\n\ndotted = keras.layers.Dot(2)([user_embedded, movie_embedded])\nout = keras.layers.Flatten()(dotted)\n\nmodel = keras.Model(\n    inputs = [user_id_input, movie_id_input],\n    outputs = out,\n)\nmodel.compile(\n    tf.train.AdamOptimizer(0.001),\n    loss='MSE',\n    metrics=['MAE'],\n)\nmodel.summary(line_length=88)\n'''\n________________________________________________________________________________________\nLayer (type)                 Output Shape       Param #   Connected to                  \n========================================================================================\nuser_id (InputLayer)         (None, 1)          0                                       \n________________________________________________________________________________________\nmovie_id (InputLayer)        (None, 1)          0                                       \n________________________________________________________________________________________\nuser_embedding (Embedding)   (None, 1, 8)       1107952   user_id[0][0]                 \n________________________________________________________________________________________\nmovie_embedding (Embedding)  (None, 1, 8)       213952    movie_id[0][0]                \n________________________________________________________________________________________\ndot (Dot)                    (None, 1, 1)       0         user_embedding[0][0]          \n                                                          movie_embedding[0][0]         \n________________________________________________________________________________________\nflatten (Flatten)            (None, 1)          0         dot[0][0]                     \n========================================================================================\nTotal params: 1,321,904\nTrainable params: 1,321,904\nNon-trainable params: 0\n________________________________________________________________________________________\n'''\n```\n\n让我们训练它。\n\n```py\nhistory = model.fit(\n    [df.userId, df.movieId],\n    df.y,\n    batch_size=5000,\n    epochs=20,\n    verbose=0,\n    validation_split=.05,\n);\n```\n\n让我们将这个模型随时间的误差，与我们在上一课中训练的深度神经网络进行比较：\n\n![](/img/learn/embeddings/emb-5.png)\n\n我们新的，更简单的模型（蓝色）看起来非常好。\n\n然而，即使我们的嵌入相当小，两种模型都会产生一些明显的过拟合。 也就是说，训练集上的误差 - 实线 - 明显好于看不见的数据。 我们将在练习中尽快解决这个问题。\n\n### 你的回合\n\n前往[练习笔记本](https://www.kaggle.com/kernels/fork/1598589)，进行矩阵分解的实践练习。\n\n## 三、使用 Gensim 探索嵌入\n\n早些时候，我们训练了一个模型，使用一个网络，它带有为每个电影和用户学习的嵌入，来预测用户为电影提供的评分。 嵌入是强大的！但他们实际如何工作？\n\n以前，我说嵌入捕获了它们所代表的对象的“含义”，并发现了有用的潜在结构。 让我们来测试吧！\n\n### 查询嵌入\n\n让我们加载我们之前训练过的模型，这样我们就可以研究它学到的嵌入权重。\n\n```py\nimport os\n\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nimport tensorflow as tf\nfrom tensorflow import keras\n\ninput_dir = '../input/movielens-preprocessing'\nmodel_dir = '../input/movielens-spiffy-model'\nmodel_path = os.path.join(model_dir, 'movie_svd_model_32.h5')\nmodel = keras.models.load_model(model_path)\n```\n\n嵌入权重是模型内部的一部分，因此我们必须进行一些挖掘才能访问它们。 我们将获取负责嵌入电影的层，并使用`get_weights()`方法获取其学习的权重。\n\n```py\nemb_layer = model.get_layer('movie_embedding')\n(w,) = emb_layer.get_weights()\nw.shape\n# (26744, 32)\n```\n\n对于那么多电影，我们的权重矩阵有 26,744 行。 每行是 32 个数字 - 我们的电影嵌入的大小。\n\n我们来看一个示例电影向量：\n\n```py\nw[0]\n'''\narray([-0.08716497, -0.25286013, -0.52679837, -0.2602235 , -0.4349191 ,\n       -0.48805636, -0.30346015, -0.1416321 ,  0.08305884, -0.17578898,\n       -0.36220485,  0.14578693,  0.37118354, -0.02961254, -0.063666  ,\n       -0.5223456 ,  0.0526049 ,  0.47991064, -0.19034313, -0.3271599 ,\n        0.32792446, -0.3794548 , -0.55778086, -0.42602876,  0.14532137,\n        0.21002969, -0.32203963, -0.46950188, -0.22500233, -0.08298543,\n       -0.00373308, -0.3885791 ], dtype=float32)\n'''\n```\n\n这是什么电影的嵌入？ 让我们加载我们的电影元数据的数据帧。\n\n```py\nmovies_path = os.path.join(input_dir, 'movie.csv')\nmovies_df = pd.read_csv(movies_path, index_col=0)\nmovies_df.head()\n```\n\n\n| | movieId | title | genres | key | year | n_ratings | mean_rating |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| 0 | 0 | Toy Story | Adventure|Animation|Children|Comedy|Fantasy | Toy Story | 1995 | 49695 | 3.921240 |\n| 1 | 1 | Jumanji | Adventure|Children|Fantasy | Jumanji | 1995 | 22243 | 3.211977 |\n| 2 | 2 | Grumpier Old Men | Comedy|Romance | Grumpier Old Men | 1995 | 12735 | 3.151040 |\n| 3 | 3 | Waiting to Exhale | Comedy|Drama|Romance | Waiting to Exhale | 1995 | 2756 | 2.861393 |\n| 4 | 4 | Father of the Bride Part II | Comedy | Father of the Bride Part II | 1995 | 12161 | 3.064592 |\n\n当然，这是《玩具总动员》！ 我应该在任何地方认出这个向量。\n\n好吧，我很滑稽。此时很难利用这些向量。 我们从未告诉模型如何使用任何特定嵌入维度。 我们只让它学习它认为有用的任何表示。\n\n那么我们如何检查这些表示是否合理且连贯？\n\n### 向量相似度\n\n测试它的一种简单方法是，查看嵌入空间中电影对有多么接近或远离。 嵌入可以被认为是智能的距离度量。 如果我们的嵌入矩阵是良好的，它应该将类似的电影（如《玩具总动员》和《怪物史莱克》）映射到类似的向量。\n\n```py\ni_toy_story = 0\ni_shrek = movies_df.loc[\n    movies_df.title == 'Shrek',\n    'movieId'\n].iloc[0]\n\ntoy_story_vec = w[i_toy_story]\nshrek_vec = w[i_shrek]\n\nprint(\n    toy_story_vec,\n    shrek_vec,\n    sep='\\n',\n)\n'''\n[-0.08716497 -0.25286013 -0.52679837 -0.2602235  -0.4349191  -0.48805636\n -0.30346015 -0.1416321   0.08305884 -0.17578898 -0.36220485  0.14578693\n  0.37118354 -0.02961254 -0.063666   -0.5223456   0.0526049   0.47991064\n -0.19034313 -0.3271599   0.32792446 -0.3794548  -0.55778086 -0.42602876\n  0.14532137  0.21002969 -0.32203963 -0.46950188 -0.22500233 -0.08298543\n -0.00373308 -0.3885791 ]\n[ 0.0570179   0.5991162  -0.71662885  0.22245468 -0.40536046 -0.33602375\n -0.24281627  0.08997302  0.03362623 -0.12569055 -0.2764452  -0.12710975\n  0.48197436  0.2724923   0.01551001 -0.20889504 -0.04863157  0.39106563\n -0.24811408 -0.05642252  0.24475795 -0.53363544 -0.2281187  -0.17529544\n  0.21050802 -0.37807122  0.03861505 -0.27024794 -0.24332719 -0.17732081\n  0.07961234 -0.39079434]\n'''\n```\n\n逐个维度地比较，这些看起来大致相似。 如果我们想为它们的相似度分配一个数字，我们可以计算这两个向量之间的欧氏距离。 （这是我们传统的“乌鸦飞过的”两点之间的距离的概念。容易在 1,2 或 3 维上进行研究。在数学上，我们也可以将它扩展到 32 维，虽然需要好运来可视化它。）\n\n```py\nfrom scipy.spatial import distance\n\ndistance.euclidean(toy_story_vec, shrek_vec)\n# 1.4916094541549683\n```\n\n这与我们认为非常不同的一对电影相比如何？\n\n```py\ni_exorcist = movies_df.loc[\n    movies_df.title == 'The Exorcist',\n    'movieId'\n].iloc[0]\n\nexorcist_vec = w[i_exorcist]\n\ndistance.euclidean(toy_story_vec, exorcist_vec)\n# 2.356588363647461\n```\n\n更远了，和我们期待的一样。\n\n### 余弦距离\n\n如果你看看[`scipy.spatial`模块的文档](https://docs.scipy.org/doc/scipy-0.14.0/reference/spatial.distance.html)，你会发现人们用于不同任务的距离，实际上有很多不同的衡量标准。\n\n在判断嵌入的相似性时，使用[余弦相似性](https://en.wikipedia.org/wiki/Cosine_similarity)更为常见。\n\n简而言之，两个向量的余弦相似度范围从 -1 到 1，并且是向量之间的角度的函数。 如果两个向量指向同一方向，则它们的余弦相似度为 1。如果它们指向相反的方向，它为 -1。 如果它们是正交的（即成直角），则它们的余弦相似度为 0。\n\n余弦距离定义为 1 减去余弦相似度（因此范围从 0 到 2）。\n\n让我们计算电影向量之间的几个余弦距离：\n\n```py\nprint(\n    distance.cosine(toy_story_vec, shrek_vec),\n    distance.cosine(toy_story_vec, exorcist_vec),\n    sep='\\n'\n)\n'''\n0.3593705892562866\n0.811933159828186\n'''\n```\n\n> 注：为什么在使用嵌入时常用余弦距离？ 与许多深度学习技巧一样，简短的答案是“凭经验，它能用”。 在即将进行的练习中，你将进行一些实践调查，更深入地探讨这个问题。\n\n哪部电影与《玩具总动员》最相似？ 在嵌入空间中哪些电影落在 Psycho 和 Scream 之间？ 我们可以编写一堆代码来解决这样的问题，但这样做非常繁琐。 幸运的是，已经有一个库可以完成这类工作：Gensim。\n\n## 使用 Gensim 探索嵌入\n\n我将使用我们的模型的电影嵌入和相应电影的标题，来实例化`WordEmbeddingsKeyedVectors`。\n\n> 注：你可能会注意到，Gensim 的文档及其许多类和方法名称都指的是词嵌入。 虽然库最常用于文本领域，但我们可以使用它来探索任何类型的嵌入。\n\n```py\nfrom gensim.models.keyedvectors import WordEmbeddingsKeyedVectors\n\n# 将数据集中的电影限制为至少具有这么多评分\nthreshold = 100\nmainstream_movies = movies_df[movies_df.n_ratings >= threshold].reset_index(drop=True)\n\nmovie_embedding_size = w.shape[1]\nkv = WordEmbeddingsKeyedVectors(movie_embedding_size)\nkv.add(\n    mainstream_movies['key'].values,\n    w[mainstream_movies.movieId]\n)\n```\n\n好的，哪个电影与《玩具总动员》最相似？\n\n```py\nkv.most_similar('Toy Story')\n'''\n/opt/conda/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n  if np.issubdtype(vec.dtype, np.int):\n[('Toy Story 2', 0.9583659172058105),\n ('Toy Story 3', 0.9159570932388306),\n ('Finding Nemo', 0.882755696773529),\n ('Monsters, Inc.', 0.8684015870094299),\n (\"A Bug's Life\", 0.8322919607162476),\n ('The Incredibles', 0.8271597623825073),\n ('Ratatouille', 0.8149864673614502),\n ('Up', 0.802034318447113),\n ('WALL·E', 0.7794805765151978),\n ('The Iron Giant', 0.7664535641670227)]\n'''\n```\n\n哇，这些都很棒！ 《玩具总动员 2》是与玩具总动员最相似的电影，这是完全合理的。 其余大多数都是具有类似计算机动画风格的动画儿童电影。\n\n所以它学到了关于三维动画儿童电影的一些东西，但也许这只是一个侥幸。 让我们来看看几个不同类型的电影的最近邻居：\n\n```\n/opt/conda/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n  if np.issubdtype(vec.dtype, np.int):\n```\n\n![](/img/learn/embeddings/emb-6.png)\n\n小众的性爱剧，风骚的半吊子喜剧，老派音乐剧，超级英雄电影......我们的嵌入能够支持各种各样的电影类型！\n\n### 语义向量数学\n\n`most_similar`方法接受可选的第二个参数`negative`。 如果我们调用`kv.most_similar(a, b)`，那么它将找到最接近`a-b`的向量，而不是找到最接近`a`的向量。\n\n你为什么想这么做？ 事实证明，对嵌入向量进行加法和减法通常会产生令人惊讶的有意义的结果。 例如，你将如何填写以下等式？\n\n```\nScream = Psycho + ________\n```\n\nScream 和 Psycho 的相似之处在于它们是恐怖片和惊悚片之间的某个地方的暴力恐怖电影。 最大的区别是 Scream 有喜剧元素。 因此，如果你将 Psycho 与喜剧结合起来，我会说 Scream 就是你所得到的。\n\n但我们实际上可以通过向量数学（在重新排列之后）让 Gensim 为我们填补空白：\n\n```\n________ = Scream - Psycho\n```\n\n```py\nkv.most_similar(\n    positive = ['Scream'],\n    negative = ['Psycho (1960)']\n)\n'''\n/opt/conda/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n  if np.issubdtype(vec.dtype, np.int):\n[('Scream 3', 0.6535503268241882),\n ('Scream 2', 0.6417772769927979),\n ('Piranha (Piranha 3D)', 0.6411199569702148),\n ('Freddy vs. Jason', 0.6275623440742493),\n ('Final Destination 5', 0.6264907121658325),\n ('Booty Call', 0.6207411289215088),\n (\"Charlie's Angels\", 0.6146069765090942),\n ('Mortal Kombat', 0.6145076155662537),\n ('Deuce Bigalow: Male Gigolo', 0.6140967607498169),\n ('Final Destination 2', 0.612423300743103)]\n'''\n```\n\n如果你熟悉这些电影，你会发现，从 Psycho 到 Scream 的缺失成分是喜剧（也是 90 年代后期的青少年电影）。\n\n### 类比解决\n\n用于进入美国大学和学院的 SAT 考试提出了类似的问题：\n\n```\nshower : deluge :: _____ : stare\n```\n\n（意思是“shower”（淋浴）对于“deluge”（洪水），相当于“_____”对于“stare”（凝视））\n\n为了解决这个问题，我们找到了“deluge”和“shower”之间的关系，并将其应用于“stare”。 “shower”是一种温和的“deluge”形式。 什么是温和的“stare”的形式？ 这里一个好的答案是“glance”（一瞥）或“look”（看）。\n\n令人惊讶的是，这种方法很有效，但人们发现这些通常可以通过单词嵌入的简单向量数学来有效地解决。 我们可以通过嵌入来解决电影类比问题吗？ 我们试试吧。这样如何：\n\n```\nBrave : Cars 2 :: Pocahontas : _____\n```\n\n答案不明确。 一种解释是，《勇敢传说》（Brave）就像《赛车总动员 2》（Cars 2），除了后者主要针对男孩，而前者可能对女孩更具吸引力，因为它是女性主角。 所以也许答案应该像《风中奇缘》（Pocahontas）一样，90 年代中期的传统动画儿童电影，但更像是一部“男孩电影”。《大力士》？《狮子王》？\n\n让我们问一下他们的想法。\n\n在向量数学方面，我们可以将其构建为......\n\n```\nCars 2 = Brave + X\n_____  = Pocahontas + X\n```\n\n重新排列之后，我们得到：\n\n```\n____ = Pocahontas + (Cars 2 - Brave)\n```\n\n我们可以通过将两部电影（《风中奇缘》和《赛车总动员 2》）传递给`most_similar`的`positive`，将《勇敢传说》作为`negative`参数，来解决这个问题：\n\n```py\nkv.most_similar(\n    ['Pocahontas', 'Cars 2'],\n    negative = ['Brave']\n)\n'''\n/opt/conda/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n  if np.issubdtype(vec.dtype, np.int):\n[(\"A Kid in King Arthur's Court\", 0.8660464882850647),\n ('Land Before Time III: The Time of the Great Giving', 0.8655920624732971),\n ('Free Willy 2: The Adventure Home', 0.8606677651405334),\n ('3 Ninjas Knuckle Up', 0.8496973514556885),\n ('3 Ninjas Kick Back', 0.8479241132736206),\n ('The Return of Jafar', 0.8474882245063782),\n (\"Beethoven's 2nd\", 0.8443870544433594),\n ('Air Bud: Golden Receiver', 0.84358811378479),\n ('Meet the Deedles', 0.8370730876922607),\n ('All Dogs Go to Heaven 2', 0.8368842601776123)]\n'''\n```\n\n这与我们的预测无关：4 部最接近的电影确实是 90 年代的儿童动画电影。 在那之后，结果有点令人困惑。\n\n我们的模型是错的，还是我们是错的？ 在《赛车总动员 2》和《勇敢传说》之间，我们未能解释的另一个区别是前者是续集，而后者则不是。 我们的结果中有 7/10 也是续集。 这告诉我们关于我们学习的嵌入的一些有趣内容（最终，关于预测电影偏好的问题）。 “Sequelness”是我们模型的一个重要特性 - 这表明我们数据中的一些变化，是因为有些人倾向于比其他人更喜欢续集。\n\n### 你的回合\n\n前往[练习笔记本](https://www.kaggle.com/kernels/fork/1598893)，进行一些动手实践，使用 gensim 探索嵌入。\n\n## 四、将 t-SNE 用于可视化\n\n在上一课中，我们查看了我们学习的电影嵌入的一些示例，测量了电影对之间的距离，查找了与某些电影最相似的电影，并且通过向量数学组合了电影语义。 这些是调试嵌入模型或理解嵌入模型的好方法。 但它也非常耗时。\n\n在本课程中，你将学习如何使用 t-SNE 算法可视化嵌入。 这是一种很好的廉价技术，用于理解嵌入的本质。\n\n### t-SNE\n\n可视化 1 维或 2 维的数据很容易 - 但目前尚不清楚如何可视化 8 维或 32 维的嵌入。 t-SNE 是一种降维算法，通常用于可视化。 它学习从一组高维向量到较小维数（通常为 2）的空间映射，这有望很好地表示高维空间。\n\n是什么让映射成为“良好的表示”？ 简而言之，t-SNE 试图确保如果高维向量`u`和`v`靠近在一起，则`map(u)`和`map(v)`在 2d 映射空间中靠近在一起。\n\n### 代码\n\n首先，我们将加载我们的预训练嵌入，就像之前一样。\n\n```py\n%matplotlib inline\nimport random\nimport os\n\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nimport tensorflow as tf\nfrom tensorflow import keras\n\ninput_dir = '../input/movielens-preprocessing'\nmodel_dir = '../input/movielens-spiffy-model'\nmodel_path = os.path.join(model_dir, 'movie_svd_model_32.h5')\n\nmodel = keras.models.load_model(model_path)\nemb_layer = model.get_layer('movie_embedding')\n(w,) = emb_layer.get_weights()\n\nmovies_path = os.path.join(input_dir, 'movie.csv')\nmovies_df = pd.read_csv(movies_path, index_col=0)\n```\n\n正如我们在前面的课程中看到的那样，我们的数据集中有很多不起眼的电影，评分很少（有时只有一个）。 我们对这些电影知之甚少，因为它们的嵌入效果和随机一样。 我们可以通过仅仅选择满足一定流行度阈值的电影，来弄清楚我们的可视化。\n\n```py\nthreshold = 100\nmainstream_movies = movies_df[movies_df.n_ratings >= threshold].reset_index(drop=True)\nprint(\"Went from {} to {} movies after applying threshold\".format(\n    len(movies_df), len(mainstream_movies),\n))\nw_full = w\nw = w[mainstream_movies.movieId]\ndf = mainstream_movies\n# 在应用阈值后，电影从 26744 变为 8546 部\n```\n\n我们将使用 scikit-learn 的 t-SNE 实现。\n\n我提到 t-SNE 在特征空间中试图保持实体之间的“接近度”。 我们在之前的课程中看到，有许多竞争性的距离概念。 默认情况下，t-SNE 使用欧氏距离。 但是因为已知余弦距离适用于嵌入，我们将在创建模型时传递`metric =\"cosine\"`。\n\n```py\nfrom sklearn.manifold import TSNE\n\n# 1,000 次迭代的默认值可以得到很好的结果，\n# 但是我的训练时间更长，只是为了一些微小的改进。 \n# 注意：这需要近一个小时！\ntsne = TSNE(random_state=1, n_iter=15000, metric=\"cosine\")\n\nembs = tsne.fit_transform(w)\n# 为方便起见，添加到数据帧\ndf['x'] = embs[:, 0]\ndf['y'] = embs[:, 1]\n```\n\n这是我们将电影映射到的二维向量样本：\n\n```py\nembs[:5]\n'''\narray([[ -93.78184  ,   74.296936 ],\n       [ -78.09159  , -107.294334 ],\n       [  27.506392 ,  -73.33844  ],\n       [  -7.8512335,  -82.217896 ],\n       [ -10.345706 ,  -71.288704 ]], dtype=float32)\n'''\n```\n\n这种降维的全部意义在于可视化，所以让我们使用 matplotlib 绘制我们电影的散点图，使用我们新的二维映射。\n\n```py\nFS = (10, 8)\nfig, ax = plt.subplots(figsize=FS)\n# 使点变得半透明，以便我们可以直观地识别具有高密度重叠点的区域\nax.scatter(df.x, df.y, alpha=.1);\n```\n\n![](/img/learn/embeddings/emb-7.png)\n\n### 它有效嘛\n\n单凭形状很难判断。 良好的理智检查是识别，我们强烈认为应该靠近的一些电影分组，看看它们是否在二维空间中接近。\n\n例如，所有的哈利波特电影都应该互相接近，对吧？\n\n```py\n# 这个和其他几个辅助函数在上面的代码单元中定义。\n# 如果你对它们的实现方式感到好奇，请点击上面的“代码”按钮。\nplot_by_title_pattern('Harry Potter', figsize=(15, 9), bg_alpha=.05, text=False);\n```\n\n![](/img/learn/embeddings/emb-8.png)\n\n上面的绘图中，8 个哈利波特电影中的每一个都有一个绿点 - 但它们是如此接近，它们无法在这个刻度上区分。 是个好的标志！\n\n让我们放大一下，仔细看看。\n\n```py\nplot_region_around('Harry Potter and the Order of the Phoenix', 4);\n```\n\n![](/img/learn/embeddings/emb-9.png)\n\n哈利波特的电影不仅紧密聚集在一起，而且大致按发布顺序排列！\n\n### 局部和全局结构\n\nt-SNE 的一个关键特性使它非常适合可视化，它擅长在多个尺度上捕获簇。 我们已经看到，我们的映射成功捕获了小而紧凑的局部结构。 那些包含更多松散的相关电影的大型结构呢？\n\n我们在上面已经看到了这方面的一个小例子：与哈利波特电影最接近的邻居是饥饿游戏系列的电影 - 另一组基于一系列青年幻想小说的电影。这说得通！\n\n小众流派如何？ 纪录片落在哪里？\n\n```py\ndocs = df[ (df.genres == 'Documentary') ]\nplot_with_annotations(docs.index, text=False, alpha=.4, figsize=(15, 8));\n```\n\n![](/img/learn/embeddings/emb-10.png)\n\n太好了！ 它不是一个紧密的簇，但这里肯定有较强的规律。\n\n并且重申一下：我们从未真正将类型展示给模型作为特征。 它无法读取标题，并看到《哈利波特和魔法石》和《哈利波特和密室》属于同一系列。 它设法获取这些潜在的模式并将它们合并到嵌入空间中，只需看到数据点，例如“用户 5299 给电影 806 评分为 4.5”。 非常好！\n\n这是另一个稍微复杂的类型实验：可视化所有电影，其类型是`{喜剧，戏剧，浪漫}`的一部分（即喜剧，戏剧，浪漫，戏剧，浪漫剧，romcoms 和......我猜是“dromcoms”？）\n\n![](/img/learn/embeddings/emb-11.png)\n\n这是最大规模的结构的一个很棒的例子。 戏剧主要在上半部分，而喜剧主要在另一半（浪漫片的分布更加分散）。\n\n### 你的回合\n\n前往[练习笔记本](https://www.kaggle.com/kernels/fork/1599029)进行一些实践练习，使用 t-SNE 可视化嵌入。\n\n## 扩展阅读\n\n我们使用 t-SNE 模型的开箱即用的默认参数取得了良好的效果，但根据你的数据特征，你可能不会那么幸运。\n\nt-SNE 不是简单的闭式数学运算。 你正在训练模型，使用随机梯度下降来最小化一些非凸损失函数。 可能需要一段时间，需要一点折腾。 你甚至可以在使用相同参数训练的两个 t-SNE 模型之间看到非常不同的结果（如果你想要可重复性，则设置固定的`random_state`）。\n\n如果你在尝试训练 t-SNE 模型时得到的结果不令人满意，或者你只是想了解更多数学基础和实现，那么下面的链接会提供一些你可能会觉得有用的信息。\n\n+   如果你对 t-SNE 的更深入的数学细节感兴趣，我强烈建议你查看 [Laurens van der Maaten 和 Geoff Hinton 向世界介绍 t-SNE 的原始论文](http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf)。\n+   [如何使用 t-SNE](https://distill.pub/2016/misread-tsne/) 有效地展示了一些令人难以置信的实时交互式示例，允许你将 t-SNE 应用于各种合成数据集并实时观察训练，并看到改变参数的效果，例如 perplexity。\n+   [sklearn TSNE 文档](http://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)提供了每个参数含义的详细信息，以及一些设置它们的提示。\n    +   另请参阅：[scikit-learn 的 t-SNE 用户指南](http://scikit-learn.org/stable/modules/manifold.html#t-sne)\n+   [t-SNE FAQ](https://lvdmaaten.github.io/tsne/#faq) 由 Laurens van der Maaten 撰写\n"
  },
  {
    "path": "docs/Kaggle/learn/intermediate-machine-learning/1.md",
    "content": "# Kaggle 官方教程：机器学习中级1 课程介绍\n> 原文：[Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) > [Introduction](https://www.kaggle.com/alexisbcook/introduction)\n> \n> 译者：[Leytton](https://github.com/Leytton)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPS：水平有限，欢迎交流指正（Leytton@126.com）\n\n## 1、课程介绍\n欢迎来到Kaggle Learning《机器学习中级》微课程！\n\n如果你有一些机器学习的基础，并且你想学习如何快速提高模型的质量，那么你就来对地方了！在这个微型课程中，你将学习如何：\n\n - 处理现实数据集中常见的数据类型(`缺失的值、分类变量`)，\n - 设计`pipelines`来提高机器学习代码的质量，\n - 使用先进的技术进行模型验证(`交叉验证`)，\n - 建立最先进的模型，广泛用于赢得Kaggle比赛(`XGBoost`)，和\n - 避免常见和重要的数据科学错误(`泄漏`)。\n \n在此过程中，你将通过使用各个新主题的真实数据完成实际操作来巩固你的知识。实际操作数据来自于赛题 [Housing Prices Competition for Kaggle Learn Users](https://www.kaggle.com/c/home-data-for-ml-course), 你将使用79个不同的统计变量(如屋顶类型、卧室数量和浴室数量)来预测房价。通过提交预测结果，观察你在排行榜上的名次上升！\n\n![在这里插入图片描述](/img/learn/intermediate-machine-learning/1.1.png)\n\n## 2、先决条件\n如果你以前构建过机器学习模型，并且熟悉模型验证、欠拟合和过拟合以及随机森林等主题，那么你已经为这门微型课程做好了准备。\n\n如果你对机器学习完全陌生，请学习我们的微课程[《机器学习入门》](https://leytton.blog.csdn.net/article/details/101154693)，它涵盖了机器学习的基础知识。\n\n## 3、去吧，皮卡丘\n继续[第一个练习](https://www.kaggle.com/kernels/fork/3370272)，学习如何向Kaggle竞赛提交预测结果，并确定在开始之前可能需要检查的内容。"
  },
  {
    "path": "docs/Kaggle/learn/intermediate-machine-learning/2.md",
    "content": "# Kaggle 官方教程：机器学习中级2 缺失值处理\n> 原文：[Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) > [Missing Values](https://www.kaggle.com/alexisbcook/missing-values)\n> \n> 译者：[Leytton](https://github.com/Leytton)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPS：水平有限，欢迎交流指正（Leytton@126.com）\n\n在本课程中，你将学习三种处理缺失值的方法。然后使用实际数据集比较这些方法的效果。\n\n## 1、介绍\n\n造成数据丢失的原因有很多。例如，\n\n- 两间卧室的房子不包括第三间卧室的价值。\n- 调查对象可能选择不分享其收入。\n\n大多数机器学习库(包括`scikit-learn`)在试图使用缺失值的数据构建模型时都会出现错误。因此，你需要选择下面的策略之一。\n\n## 2、三种方法\n**1) 一个简单的选择：删除缺少值的列**\n\n最简单的选择是删除缺少值的列。\n\n![tut2_approach1](/img/learn/intermediate-machine-learning/2.1.png)\n\n除非删除列中的大多数值都丢失了，否则使用这种方法将丢失许多潜在价值的信息。举个极端的例子，假如有一个10,000行的数据集，其中一个重要的列缺少一条数据。这种方法将完全删除该列！\n\n**2) 一个更好的选择：填充**\n\n用一些数字填充缺失的值。例如，我们可以用平均值填充。\n\n![在这里插入图片描述](/img/learn/intermediate-machine-learning/2.2.png)\n\n在大多数情况下，填充的值不一定正确，但它通常会比完全删除该列数据要好。\n\n**3) 填充扩展**\n\n`填充`是标准的方法，通常效果很好。然而，填充的值可能高于或低于实际值(数据集中没有收集这些值)。或者缺少值的行在某些方面可能是唯一的。在这种情况下，你的模型将考虑哪些值是最初丢失的，从而做出更好的预测。\n\n![在这里插入图片描述](/img/learn/intermediate-machine-learning/2.3.png)\n\n在这种方法中，我们像以前一样输入缺失的值。此外，新增一列用于记录哪条数据是缺失填充的。\n\n在某些情况下，这将非常有效。但某些情况，这没有一点用。\n\n## 3、案例\n\n在本例中，我们将使用 [Melbourne Housing](https://www.kaggle.com/dansbecker/melbourne-housing-snapshot/home) 数据集。我们的模型将使用房间数量和土地面积等信息来预测房价。\n\n我们不会关注数据加载步骤。相反，你可以想象你已经拥有了`X_train`、`X_valid`、`y_train`和`y_valid`中的训练和验证数据。\n\n**定义函数来评估每种方法的效果**\n\n我们定义了一个函数`score_dataset()`来比较处理缺失值的不同方法。该函数计算`随机森林模型`的`平均绝对误差(MAE)`。\n\n**方法1的得分(删除缺少值的列)**\n\n由于我们同时处理训练集和验证集，所以要注意在两个数据框中删除相同的列。\n\n```python\n# 获取缺少值的列名称\ncols_with_missing = [col for col in X_train.columns\n                     if X_train[col].isnull().any()]\n\n# 删除训练和验证数据中的列\nreduced_X_train = X_train.drop(cols_with_missing, axis=1)\nreduced_X_valid = X_valid.drop(cols_with_missing, axis=1)\n\nprint(\"MAE from Approach 1 (Drop columns with missing values):\")\nprint(score_dataset(reduced_X_train, reduced_X_valid, y_train, y_valid))\n```\n输出结果：\n```\nMAE from Approach 1 (Drop columns with missing values):\n183550.22137772635\n```\n**方法2得分(填充)**\n\n接下来，我们使用`SimpleImputer`用每一列的平均值填充缺失的值。\n\n虽然它很简单，但是填充平均值的方法通常很好用(不同数据集有所差异)。尽管统计学家已经尝试了更复杂的方法来确定填充值(例如回归填充)，但一旦将结果插入复杂的机器学习模型，这些复杂的策略通常不会带来额外的好处。\n\n```python\nfrom sklearn.impute import SimpleImputer\n\n# 填充\nmy_imputer = SimpleImputer()\nimputed_X_train = pd.DataFrame(my_imputer.fit_transform(X_train))\nimputed_X_valid = pd.DataFrame(my_imputer.transform(X_valid))\n\n# 填充移除了列名;补回来\nimputed_X_train.columns = X_train.columns\nimputed_X_valid.columns = X_valid.columns\n\nprint(\"MAE from Approach 2 (Imputation):\")\nprint(score_dataset(imputed_X_train, imputed_X_valid, y_train, y_valid))\n```\n输出结果：\n```\nMAE from Approach 2 (Imputation):\n178166.46269899711\n```\n我们看到方法2的`MAE`比方法1低，所以方法2在这个数据集中表现得更好。\n\n\n**方法3得分(填充扩展)**\n\n接下来，我们填充缺失的值，同时记录哪些值是填充的。\n```python\n# Make copy to avoid changing original data (when imputing)\nX_train_plus = X_train.copy()\nX_valid_plus = X_valid.copy()\n\n# Make new columns indicating what will be imputed\nfor col in cols_with_missing:\n    X_train_plus[col + '_was_missing'] = X_train_plus[col].isnull()\n    X_valid_plus[col + '_was_missing'] = X_valid_plus[col].isnull()\n\n# Imputation\nmy_imputer = SimpleImputer()\nimputed_X_train_plus = pd.DataFrame(my_imputer.fit_transform(X_train_plus))\nimputed_X_valid_plus = pd.DataFrame(my_imputer.transform(X_valid_plus))\n\n# Imputation removed column names; put them back\nimputed_X_train_plus.columns = X_train_plus.columns\nimputed_X_valid_plus.columns = X_valid_plus.columns\n\nprint(\"MAE from Approach 3 (An Extension to Imputation):\")\nprint(score_dataset(imputed_X_train_plus, imputed_X_valid_plus, y_train, y_valid))\n```\n输出结果：\n```\nMAE from Approach 3 (An Extension to Imputation):\n178927.503183954\n```\n\n正如我们所看到的，方法3的效果略差于方法2。\n\n**那么，为什么填充比删除列更好呢?**\n\n训练数据有10864行和12列，其中3列包含丢失的数据。对于每一列，缺少的条目不到一半。因此，删除列会删除很多有用的信息，因此填充方法效果会更好。\n\n```python\n# Shape of training data (num_rows, num_columns)\nprint(X_train.shape)\n\n# Number of missing values in each column of training data\nmissing_val_count_by_column = (X_train.isnull().sum())\nprint(missing_val_count_by_column[missing_val_count_by_column > 0])\n```\n输出结果：\n```\n(10864, 12)\nCar               49\nBuildingArea    5156\nYearBuilt       4307\ndtype: int64\n```\n## 4、结论\n一般来说，比起简单地删除具有缺失值的列(在方法1中)，填充缺失值(在方法2和方法3中)会得到更好的效果。\n\n## 5、去吧，皮卡丘\n在[练习中](https://www.kaggle.com/kernels/fork/3370280)比较处理缺失值的方法。\n\n"
  },
  {
    "path": "docs/Kaggle/learn/intermediate-machine-learning/3.md",
    "content": "# Kaggle 官方教程：机器学习中级3 分类变量\n> 原文：[Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) > [Categorical Variables](https://www.kaggle.com/alexisbcook/categorical-variables)\n> \n> 译者：[Leytton](https://github.com/Leytton)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPS：水平有限，欢迎交流指正（Leytton@126.com）\n\n在本教程中，你将了解什么是分类变量，以及处理这类数据的三种方法。\n## 1、介绍\n分类变量类似于枚举，拥有特定数量的值类型。\n\n - 比如一项调查，询问你多久吃一次早餐，并提供四个选项:“从不”、“很少”、“大多数日子”或“每天”。在本例中，数据是分类的，因为答案属于一组固定的类别。\n    \n - 如果对人们所拥有的汽车品牌进行调查，回答可以分为“本田”、“丰田”和“福特”。在本例中，数据也是分类的。\n\n如果您没有预处理这些分类变量，就将这些变量应用于机器学习模型中，大多数情况您将得到一个错误结果。在本教程中，我们将比较三种预处理分类数据的方法。\n\n## 2、三种方法\n**1) 删除分类变量**\n处理分类变量最简单的方法是从数据集中删除它们。这种方法适用于该列中不包含有用信息的情况。\n\n**2) 标签编码**\n标签编码将每个变量类型标记为不同的整数。\n\n![在这里插入图片描述](/img/learn/intermediate-machine-learning/3.1.png)\n\n这种方法假设类别的顺序为:“Never”(0)<“rare”(1)<“Most days”(2)<“Every day”(3)。\n\n在本例中，这个假设是有意义的，因为对类别有个唯一的排名。 并不是所有的分类变量在值中都有一个明确的顺序，但是我们将那些有顺序的变量称为`有序变量`。对于基于树的模型(如决策树和随机森林)，有序变量的标签编码可能效果不错。\n\n**3) One-Hot 编码**\n\n“One-hot”编码创建新列，表明原始数据中每个可能值的存在(或不存在)。为了说明这点，我们举个例子：\n\n![在这里插入图片描述]((/img/learn/intermediate-machine-learning/3.2.png))\n\n在原始数据集中，“Color”是一个分类变量，包含“Red”、“Yellow”和“Green”三个类别。对应的`one-hot编码`是每个可能的值各自作为一列，原始数据集中的每一行作为一行。当原始值为“Red”时，我们在“Red”列中放入1;如果原始值是“Yellow”，则在“Yellow”列中放入1，以此类推。\n\n与`标签编码`不同，`one-hot编码`不假定类别的顺序。因此，如果在分类数据中没有明确的顺序(例如，“红色”既不比“黄色”多也不比“黄色”少)，这种方法可能会特别有效。我们把没有内在排序的分类变量称为`名义变量`。\n\n如果分类变量具有大量不同的值（超过15个），效果将不会很好。\n\n## 3、案例\n像前一篇教程一样，我们将使用 [Melbourne Housing数据集](https://www.kaggle.com/dansbecker/melbourne-housing-snapshot/home)。\n\n我们不会关注数据加载步骤。假设您已经拥有了`X_train`、`X_valid`、`y_train`和`y_valid`中的训练和验证数据。\n\n```python\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\n# Read the data\ndata = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')\n\n# Separate target from predictors\ny = data.Price\nX = data.drop(['Price'], axis=1)\n\n# Divide data into training and validation subsets\nX_train_full, X_valid_full, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2,\n                                                                random_state=0)\n\n# Drop columns with missing values (simplest approach)\ncols_with_missing = [col for col in X_train_full.columns if X_train_full[col].isnull().any()] \nX_train_full.drop(cols_with_missing, axis=1, inplace=True)\nX_valid_full.drop(cols_with_missing, axis=1, inplace=True)\n\n# \"Cardinality\" means the number of unique values in a column\n# Select categorical columns with relatively low cardinality (convenient but arbitrary)\nlow_cardinality_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and \n                        X_train_full[cname].dtype == \"object\"]\n\n# Select numerical columns\nnumerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]\n\n# Keep selected columns only\nmy_cols = low_cardinality_cols + numerical_cols\nX_train = X_train_full[my_cols].copy()\nX_valid = X_valid_full[my_cols].copy()\n```\n我们使用下面的`head()`方法查看训练数据。\n```python\nX_train.head()\n```\n输出结果：\n```\n\tType \tMethod \tRegionname \tRooms \tDistance \tPostcode \tBedroom2 \tBathroom \tLandsize \tLattitude \tLongtitude \tPropertycount\n12167 \tu \tS \tSouthern Metropolitan \t1 \t5.0 \t3182.0 \t1.0 \t1.0 \t0.0 \t-37.85984 \t144.9867 \t13240.0\n6524 \th \tSA \tWestern Metropolitan \t2 \t8.0 \t3016.0 \t2.0 \t2.0 \t193.0 \t-37.85800 \t144.9005 \t6380.0\n8413 \th \tS \tWestern Metropolitan \t3 \t12.6 \t3020.0 \t3.0 \t1.0 \t555.0 \t-37.79880 \t144.8220 \t3755.0\n2919 \tu \tSP \tNorthern Metropolitan \t3 \t13.0 \t3046.0 \t3.0 \t1.0 \t265.0 \t-37.70830 \t144.9158 \t8870.0\n6043 \th \tS \tWestern Metropolitan \t3 \t13.3 \t3020.0 \t3.0 \t1.0 \t673.0 \t-37.76230 \t144.8272 \t4217.0\n```\n接下来，我们获得训练数据中所有分类变量的列。\n\n我们通过检查每个列的数据类型(或`dtype`)来做到这一点。`object` 类型表示改列存在文本(理论上它还可以是其他东西，但对于我们的目的来说并不重要)。对于这个数据集，带有文本的列表示分类变量。\n```python\n# Get list of categorical variables\ns = (X_train.dtypes == 'object')\nobject_cols = list(s[s].index)\n\nprint(\"Categorical variables:\")\nprint(object_cols)\n```\n输出结果：\n```\nCategorical variables:\n['Type', 'Method', 'Regionname']\n```\n\n**定义函数来评估每种方法的效果**\n\n我们定义了一个函数`score_dataset()`来比较处理分类变量的不同方法。该函数计算`随机森林模型`的`平均绝对误差(MAE)`。一般来说，我们希望`MAE`越低越好！\n\n**方法1的得分(删除分类变量)**\n\n我们使用`select_dtypes()`方法删除对象列。\n```python\ndrop_X_train = X_train.select_dtypes(exclude=['object'])\ndrop_X_valid = X_valid.select_dtypes(exclude=['object'])\n\nprint(\"MAE from Approach 1 (Drop categorical variables):\")\nprint(score_dataset(drop_X_train, drop_X_valid, y_train, y_valid))\n```\n```\nMAE from Approach 1 (Drop categorical variables):\n175703.48185157913\n```\n**方法2的得分(标签编码)**\n\n`Scikit-learn`有一个`LabelEncoder`类，可以用来获取标签编码。我们循环遍历分类变量，并将标签编码器分别应用于每一列。\n```python\nfrom sklearn.preprocessing import LabelEncoder\n\n# 复制一份数据防止改变源数据 \nlabel_X_train = X_train.copy()\nlabel_X_valid = X_valid.copy()\n\n# 将标签编码器分别应用于每一列\nlabel_encoder = LabelEncoder()\nfor col in object_cols:\n    label_X_train[col] = label_encoder.fit_transform(X_train[col])\n    label_X_valid[col] = label_encoder.transform(X_valid[col])\n\nprint(\"MAE from Approach 2 (Label Encoding):\") \nprint(score_dataset(label_X_train, label_X_valid, y_train, y_valid))\n```\n```\nMAE from Approach 2 (Label Encoding):\n165936.40548390493\n```\n在上面的代码中，对于每个列，我们随机分配一个唯一整数。这是一种比提供自定义标签更简单的常见方法；然而，如果我们为所有有序变量提供更好的信息标签，效果会更好。\n\n**方法3的得分((One-Hot编码)**\n\n我们使用`scikit-learn`的`OneHotEncoder`类来获得`one-hot编码`。有许多参数可定义。\n- 设置`handle_unknown='ignore'`，以避免在验证数据包含训练数据中没有包括的值时发生错误\n- 设置`sparse=False`可以确保将已编码的列作为`numpy数组`(而不是稀疏矩阵)返回。\n\n为了使用这个编码器，我们提供了只有分类变量的数据列。举例来说，为了对训练数据进行编码，我们提供了`X_train[object_cols]`(代码中的`object_cols`表示分类变量名称，`X_train[object_cols]`包含了训练数据的所有分类变量)。\n```python\nfrom sklearn.preprocessing import OneHotEncoder\n\n# 将one-hot编码器分别应用于每一列分类变量\nOH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\nOH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[object_cols]))\nOH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[object_cols]))\n\n# One-hot编码时移除了index;补回来\nOH_cols_train.index = X_train.index\nOH_cols_valid.index = X_valid.index\n\n# 删除分类列(将替换为One-hot编码),留下编码列\nnum_X_train = X_train.drop(object_cols, axis=1)\nnum_X_valid = X_valid.drop(object_cols, axis=1)\n\n# 向数值特征添加One-hot编码列\nOH_X_train = pd.concat([num_X_train, OH_cols_train], axis=1)\nOH_X_valid = pd.concat([num_X_valid, OH_cols_valid], axis=1)\n\nprint(\"MAE from Approach 3 (One-Hot Encoding):\") \nprint(score_dataset(OH_X_train, OH_X_valid, y_train, y_valid))\n```\n输出结果：\n```\nMAE from Approach 3 (One-Hot Encoding):\n166089.4893009678\n```\n## 4、哪种方法最好?\n在本案例中，`删除分类变量`(方法1)的性能最差，因为它有最高的`MAE`分数。至于另外两种方法，由于返回的`MAE`分数值非常接近，没有太大差异。\n\n通常，`one-hot编码`(方法3)的效果最好，`删除分类变量`(方法1)的效果最差，但还得视情况而定。\n\n## 5、结论\n这个世界充满了分类数据。如果您知道如何使用这种常见的数据类型，您将成为一个更高效的数据科学家！\n\n## 6、去吧，皮卡丘\n把你的新技能运用到下面的[练习中](https://www.kaggle.com/kernels/fork/3370279)！"
  },
  {
    "path": "docs/Kaggle/learn/intermediate-machine-learning/4.md",
    "content": "# Kaggle 官方教程：机器学习中级4 Pipeline\n> 原文：[Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) > [Pipelines](https://www.kaggle.com/alexisbcook/pipelines)\n> \n> 译者：[Leytton](https://github.com/Leytton)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPS：水平有限，欢迎交流指正（Leytton@126.com）\n\n在本教程中，你将学习如何使用`pipeline`来清理你的建模代码。\n\n## 1、介绍\n`Pipeline`是一种简单的方法，能让你的数据预处理和建模步骤一步到位。\n\n很多数据科学家没有使用`pipeline`来建模，但`pipeline`有很多重要好处。包含：\n\n 1. 更精简的代码：考虑到数据处理时会造成混乱，使用`pipeline`不需要在每个步骤都特别注意训练和验证数据。\n 2. 更少的Bug：错误应用和忘记处理步骤的概率更小。\n 3. 更易产品化：把模型转化成规模化发布原型是比较难的，再此我们不做过多讨论，但`pipeline`对这个有帮助。\n 4. 模型验证更加多样化：你将会在下一个课程中看到`交叉验证`的案例。\n\n## 2、案例\n跟前面教程一样，我们将会使用 [Melbourne Housing 数据集](https://www.kaggle.com/dansbecker/melbourne-housing-snapshot/home)\n\n我们不会关注数据加载步骤。假设你已经拥有了`X_train`、`X_valid`、`y_train`和`y_valid`中的训练和验证数据。\n\n我们先使用`head()`方法瞄一眼训练数据。注意这些数据保护分类数据和缺失数据。使用pipelines将会很方便处理这两者。\n```python\nX_train.head()\n```\n输出结果：\n```\n\tType \tMethod \tRegionname \tRooms \tDistance \tPostcode \tBedroom2 \tBathroom \tCar \tLandsize \tBuildingArea \tYearBuilt \tLattitude \tLongtitude \tPropertycount\n12167 \tu \tS \tSouthern Metropolitan \t1 \t5.0 \t3182.0 \t1.0 \t1.0 \t1.0 \t0.0 \tNaN \t1940.0 \t-37.85984 \t144.9867 \t13240.0\n6524 \th \tSA \tWestern Metropolitan \t2 \t8.0 \t3016.0 \t2.0 \t2.0 \t1.0 \t193.0 \tNaN \tNaN \t-37.85800 \t144.9005 \t6380.0\n8413 \th \tS \tWestern Metropolitan \t3 \t12.6 \t3020.0 \t3.0 \t1.0 \t1.0 \t555.0 \tNaN \tNaN \t-37.79880 \t144.8220 \t3755.0\n2919 \tu \tSP \tNorthern Metropolitan \t3 \t13.0 \t3046.0 \t3.0 \t1.0 \t1.0 \t265.0 \tNaN \t1995.0 \t-37.70830 \t144.9158 \t8870.0\n6043 \th \tS \tWestern Metropolitan \t3 \t13.3 \t3020.0 \t3.0 \t1.0 \t2.0 \t673.0 \t673.0 \t1970.0 \t-37.76230 \t144.8272 \t4217.0\n```\n我们通过三个步骤来使用pipelines：\n\n**步骤1：定义处理步骤**\n\n与`pipeline`将预处理与建模步骤打包一样，我们使用`ColumnTransformer`类来将不同的步骤打包在一起。下面的代码做了两件事情：\n\n - 填充缺失数据为`数值`类型（用均值等方法填充数值类型数据）\n - 用`one-hot`编码来填充`分类`缺失数据\n\n```python\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import OneHotEncoder\n\n# 预处理数值类型数据\nnumerical_transformer = SimpleImputer(strategy='constant')\n\n# 预处理分类数据\ncategorical_transformer = Pipeline(steps=[\n    ('imputer', SimpleImputer(strategy='most_frequent')),\n    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n])\n\n# 将处理步骤打包\npreprocessor = ColumnTransformer(\n    transformers=[\n        ('num', numerical_transformer, numerical_cols),\n        ('cat', categorical_transformer, categorical_cols)\n    ])\n```\n\n**步骤2：定义模型**\n\n接下来我们使用熟悉的`RandomForestRegressor`类定义一个`随机森林`模型。\n\n```python\nfrom sklearn.ensemble import RandomForestRegressor\n\nmodel = RandomForestRegressor(n_estimators=100, random_state=0)\n```\n**步骤3：创建和评估Pipeline**\n\n最后，我们使用`Pipeline`类来定义一个打包预处理和建模步骤的pipeline。这里有些重点需要注意：\n\n - 使用pipeline，我们预处理训练数据以及拟合模型只有了一行代码。（相反，如果不使用pipeline，我们需要做填充、编码、和模型训练的步骤。如果我们需要同时处理数值和分类变量，这将非常繁琐！）\n - 我们在调用`predict()`指令时使用的`X_valid`中包含未经处理的特征值，pipeline会在预测前自动进行预处理。（然而，如果不使用pipeline，在预测前，我们需要记住对验证数据进行预处理）。\n\n```python\nfrom sklearn.metrics import mean_absolute_error\n\n# 在pipeline中打包预处理和建模代码\nmy_pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n                              ('model', model)\n                             ])\n\n# 预处理训练数据，拟合模型 \nmy_pipeline.fit(X_train, y_train)\n\n# 预处理验证数据, 获取预测值\npreds = my_pipeline.predict(X_valid)\n\n# 评估模型\nscore = mean_absolute_error(y_valid, preds)\nprint('MAE:', score)\n```\n输出结果：\n```\nMAE: 160679.18917034855\n```\n## 3、结论\n`Pipeline`在机器学习代码清理和规避错误中，尤其是复杂数据预处理的工作流，非常实用。 \n\n## 4、去吧，皮卡丘\n在接下来的[练习](https://www.kaggle.com/kernels/fork/3370278)中，使用`pipeline`来体验高级数据预处理技术并改善你的预测！\n\n"
  },
  {
    "path": "docs/Kaggle/learn/intermediate-machine-learning/5.md",
    "content": "# Kaggle 官方教程：机器学习中级5 交叉验证\n> 原文：[Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) > [Cross-Validation](https://www.kaggle.com/alexisbcook/cross-validation)\n> \n> 译者：[Leytton](https://github.com/Leytton)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPS：水平有限，欢迎交流指正（Leytton@126.com）\n\n在本节课程中，你将会学习如何使用交叉验证来评估模型性能。\n\n## 1、介绍\n机器学习是一个迭代的过程。\n\n您将面临以下选择：使用什么预测变量、使用什么类型的模型、向这些模型提供什么参数等等。目前为止，你使用验证数据（或保留数据）评估模型质量来做出这些选择。\n\n但这种方法也有一些缺点。假设你有一个5000行的数据集，通常你会保留大约20%（1000行）的数据作为验证数据。但这再模型评分中会带来随机变化，有时在一组1000行校验数据中表现良好，但在另一个1000行数据集中却不准确。\n\n在极端情况下，你可以想象在验证数据集中只有一行数据。如果你比较不同的模型，那么哪一个模型预测效果最好主要取决于运气！\n\n一般来说，验证数据集越大，评估模型质量中带来的随机性（即“噪音”）越小，越可靠。不幸的是，我们只能从训练数据中取出部分来获取更大的验证数据集，更小的训练数据集意味着更糟糕的模型！\n\n## 2、什么是交叉验证？\n在`交叉验证中`，我们使用不同的数据子集来执行建模过程，以获得模型质量的多个度量。\n\n例如，我们可以将数据分成5个部分，每个部分占整个数据集的20%。.在这种情况下，我们将数据分成5个“**folds**”。\n\n![tut5_crossval](/img/learn/intermediate-machine-learning/5.1.png)\n\n然后，我们对每个fold进行一次实验：\n\n - 在**实验1**中，我们使用第一个fold作为验证（保留）数据集，其他fold为训练数据。这给我们一个基于20%保留数据集的模型质量度量。\n - 在**实验2**中，我们保留第二个fold的数据（其他fold为训练数据）。这将得到第二个模型质量评估结果。\n - 以此类推，使用每个fold作为验证数据集，我们最终得到的模型质量，是基于所有的行数据集(即使我们不同时使用所有行)。\n\n## 3、什么时候使用交叉验证？\n交叉验证为模型质量提供了更精确的度量，这在您进行大量建模决策时尤为重要。然而，它可能需要更长的时间来运行，因为它评估了多个模型（每个fold一个）。\n\n所以，在此权衡之下，什么时候使用每种方法呢？\n\n - 对于小数据集，额外的计算并不是什么大事，你应该进行交叉验证。\n - 对于较大的数据集，一个验证数据集就够了。你的代码将会执行得更快，有足够的数据就没必要复用数据。\n\n并没有一个明确的界限来区数据集是大还是小，但如果你的模型几分钟或更短时间就能跑完，那么久值得使用交叉验证。\n\n或者，你可以运行交叉验证，看看每个实验的分数是否接近。如果每个实验都产生相同的结果，一个验证集可能就足够了。\n\n## 4、案例\n\n我们将会使用前面课程中相同的数据，加载输入数据`X`并输出数据到`y`。\n\n然后我们定义一个`pipeline`，使用`填充器`来填充缺失数据，以及`随机森林模型`来做预测。\n\n如果不使用\t`pipeline`，来交叉验证是非常困难的！使用`pipeline`将会使代码非常简单。\n```python\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.impute import SimpleImputer\n\nmy_pipeline = Pipeline(steps=[('preprocessor', SimpleImputer()),\n                              ('model', RandomForestRegressor(n_estimators=50,\n                                                              random_state=0))\n                             ])\n```\n我们使用`cross_val_score() `函数来获取交叉验证评分，通过`cv`参数来设置fold数量。\n```python\nfrom sklearn.model_selection import cross_val_score\n\n# 乘以-1，因为sklearn计算得到的是负MAE值（neg_mean_absolute_error）\nscores = -1 * cross_val_score(my_pipeline, X, y,\n                              cv=5,\n                              scoring='neg_mean_absolute_error')\n\nprint(\"MAE scores:\\n\", scores)\n```\n输出结果：\n```\nMAE scores:\n [301628.7893587  303164.4782723  287298.331666   236061.84754543\n 260383.45111427]\n```\n`scoring`参数指定了模型质量的度量方法，我们选择`负的平均绝对误差值`，`scikit-learn`文档列出了[可选值](http://scikit-learn.org/stable/modules/model_evaluation.html)。\n\n指定负MAE有点奇怪。Scikit-learn有一个约定，大的数字意味着更好的效果。在这里使用负号可以使它们与约定保持一致，尽管在其他地方几乎没有听说过。\n\n我们通常需要模型质量的单一度量来比较不同的模型，所以取全部实验的平均值。\n```python\nprint(\"Average MAE score (across experiments):\")\nprint(scores.mean())\n```\n输出结果：\n```\nAverage MAE score (across experiments):\n277707.3795913405\n```\n## 5、结论\n使用交叉验证能使模型质量更好，同时代码也更加简洁：注意我们不再需要跟踪每个的训练和验证集。尤其是对于小数据集，这是个很大的提升！\n\n## 6、去吧，皮卡丘\n\n把你的新技能运用到[下一个练习](https://www.kaggle.com/kernels/fork/3370281)中~\n"
  },
  {
    "path": "docs/Kaggle/learn/intro-to-machine-learning/1.md",
    "content": "# Kaggle 官方教程：机器学习入门1 模型是怎样工作的\n> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [How Models Work](https://www.kaggle.com/dansbecker/how-models-work)\n> \n> 译者：[Leytton](https://github.com/Leytton)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPS：水平有限，欢迎交流指正（Leytton@126.com）\n\n## 1、简介\n\n我们将首先概述机器学习模型如何工作以及如何使用它们。如果您以前做过统计建模或机器学习，这可能会让您觉得很基础。别担心，我们很快就会建立强大的模型。\n\n这门微课程将用以下场景为例来构建模型：\n\n你的表哥在房地产投机上赚了几百万美元。由于你对数据科学的兴趣，他愿意成为你的商业伙伴。他会提供资金，你负责提供模型来预测各种房子的价值。\n\n你问表哥他过去是如何预测房产价值的，他说只是凭直觉。但更多的问题表明，他从过去看到的房子中总结出价格模式，并利用这些模式对他当前考虑的新房做出预测。\n\n机器学习也是如此。 我们将从一个叫做决策树的模型开始说起。当然还有更神奇的模型可以提供更准确的预测，但是决策树很容易理解，它们是数据科学中一些最佳模型的基本构件。\n\n为了简单起见，我们将从最简单的决策树开始。\n\n![First Decision Trees](/img/learn/intro-to-machine-learning/1.1.png)\n\n如上图所示，它将房子只分为两类。房子的预测价格是同类房子的历史平均价格。\n\n我们使用数据来决定如何把房子分成两组，然后再确定每组的预测价格。从数据中捕获模式的这一步骤称为`拟合`或`训练模型`。用于`拟合模型`的数据称为`训练数据`。\n\n模型拟合的过程(例如，如何分割数据)比较复杂，我们以后再提。在模型被拟合之后，您可以将其应用于预测新房的价格。\n\n## 2、改进决策树\n\n以下两种决策树中，哪一种更有可能来自于房子训练数据的拟合?\n\n![在这里插入图片描述](/img/learn/intro-to-machine-learning/1.2.png)\n\n左边的决策树可能更有意义，因为它考虑一个事实：卧室多的房子往往比卧室少的房子售价更高。但这个模型没有考虑到影响房价的其他因素，比如卫生间的数量，地段大小，位置等。\n\n你可以使用更多`“分叉”`的决策树来考虑其他影响因素，即`“更深”`的树。一个决策树也考虑了每栋房子的总占地面积，看起来可能是这样的：\n\n![在这里插入图片描述](/img/learn/intro-to-machine-learning/1.3.png)\n\n只要沿着满足条件的分支进行选择，您可以通过跟踪决策树来预测任何房子的价格。房价将在决策树的底部点得出，我们将这个点叫做`叶节点`。\n\n叶子处的分支和值将由数据决定，接下来将检查您用到的数据。\n\n## 3、后续\n\n接下来我们将详细讲解怎么检查您的数据。\n"
  },
  {
    "path": "docs/Kaggle/learn/intro-to-machine-learning/2.md",
    "content": "# Kaggle 官方教程：机器学习入门2 数据探索\n> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [Basic Data Exploration](https://www.kaggle.com/dansbecker/basic-data-exploration)\n> \n> 译者：[Leytton](https://github.com/Leytton)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPS：水平有限，欢迎交流指正（Leytton@126.com）\n\n## 1、使用Pandas熟悉数据\n任何机器学习项目的第一步都是熟悉数据。你可以使用`Pandas`来实现。`Pandas`是数据科学家用来探索和操作数据的主要工具。大多数人在代码中将`panda`简写为`pd`，使用以下代码将其引用：\n```python\nimport pandas as pd\n```\n`Pandas`最重要的部分就是`DataFrame`了。`DataFrame`保存了类似表的数据类型，就像Excel中的工作表或SQL数据库中的表。\n\n`Pandas`具有强大的函数来实现大部分你想要的数据操作。\n\n举个例子，我们来看看澳大利亚墨尔本的房价数据。\n数据文件路径在`../input/melbourne-housing-snapshot/melb_data.csv`。\n\n我们使用以下命令来加载和查看数据：\n```python\n# 文件路径\nmelbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'\n# 读取并保存数据到DataFrame类型变量melbourne_data\nmelbourne_data = pd.read_csv(melbourne_file_path) \n# 打印数据概览\nmelbourne_data.describe()\n```\n\n![在这里插入图片描述](/img/learn/intro-to-machine-learning/2.1.png)\n\n## 2、数据描述详解\n如上图所示，结果打印了8个数据。第一个`count`显示有多少个未缺失的数据。缺失值的产生有很多原因。例如，本身只有一间卧室的房子，就不会存在第二间卧室的数据。我们重回数据缺失的主题。\n\n第二个值是`mean`，也就是平均值。`std`是标准偏差，它体现了数据分布情况。\n\n`min`和 `max` 比较好理解，分别是指`最小值`和`最大值`；\n `25%, 50%, 75% `是指，我们将数据从小到大排列，返回25%，50%，75%数据量时的数字。\n\n## 3、去吧，皮卡丘\n从[这里](https://www.kaggle.com/kernels/fork/1258954)开启你的编程实战吧~\n\n"
  },
  {
    "path": "docs/Kaggle/learn/intro-to-machine-learning/3.md",
    "content": "# Kaggle 官方教程：机器学习入门3 你的第一个机器学习模型\n> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [Your First Machine Learning Model](https://www.kaggle.com/dansbecker/your-first-machine-learning-model)\n> \n> 译者：[Leytton](https://github.com/Leytton)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPS：水平有限，欢迎交流指正（Leytton@126.com）\n\n## 1、选择建模数据\n原始数据集有太多的干扰变量，难以理解，甚至无法很好地打印出来。如何将这些数据处理为比较精简易懂呢？\n\n我们先凭直觉选择几个变量。后面的课程将向你展示使用统计技术来自动优选变量。\n\n选择变量/列前，我们先来看看数据集中有哪些列，使用`DataFrame`的`columns`属性来实现，代码如下：\n```python\nimport pandas as pd\n\nmelbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'\nmelbourne_data = pd.read_csv(melbourne_file_path) \nmelbourne_data.columns\n```\n输出结果：\n```\nIndex(['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG',\n       'Date', 'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car',\n       'Landsize', 'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude',\n       'Longtitude', 'Regionname', 'Propertycount'],\n      dtype='object')\n```\n\n - Melbourne 数据集有部分缺失(有些房子没有记录变量。)\n - 我们将在后面的教程中学习如何处理缺失值。 \n - Iowa 数据没有缺失值。\n - 现在我们将采用最简单的方法，从数据中删除掉数据缺失的房屋。\n - 现在不要太担心这个，代码如下:\n```python\n# dropna 删除有缺失的数据 (na可以看作是\"not available\")\nmelbourne_data = melbourne_data.dropna(axis=0)\n```\n\n有许多选择数据子集的方法，[《Pandas微课程》](https://www.kaggle.com/learn/pandas)将更深入地介绍这些内容，但目前我们将重点介绍两种方法。\nThere are many ways to select a subset of your data. The Pandas Micro-Course covers these in more depth, but we will focus on two approaches for now.\n\n 1. `点符号`，用来选择`\"预测目标\"`；\n 2. `列数组`, 用来选择`“特性”`。\n\n\n## 2、选择预测目标\n你可以用`点符号`来获取一个变量。这个单列存储在一个系列中，与只有单列数据的`DataFrame`非常类似。\n\n我们将使用点符号来选择要预测的列，这称为预测目标。按照惯例，预测目标我们记为`y`，将房价保存到`y`变量的代码为：\n```python\ny = melbourne_data.Price\n```\n\n## 3、选择特征值\n输入到模型中的数据列(稍后用于进行预测)称为“特性”。在我们的例子中，这些数据列是用来确定房价。有时，你将使用除预测目标之外的所有数据列作为特性。但有时候，剔除无效的特征，预测效果会更好。\n\n现在，我们将构建一个只包含几个特性的模型。稍后你将看到如何迭代和比较这些使用不同特性构建的模型。\n我们通过在括号内提供列名列表来选择多个特性。列表中的每一项都应该是一个字符串(带引号)。\n\n在数据集中提取多个特征列的代码如下（列名称应该是字符串类型）：\n\n```python\nmelbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']\n```\n按照惯例，这个数据称为X。\n```python\nX = melbourne_data[melbourne_features]\n```\n我们快速地用`describe`方法与`head `方法，来查看数据概览：\n```python\nX.describe()\n```\n输出结果：\n\n![在这里插入图片描述](/img/learn/intro-to-machine-learning/3.1.png)\n\n```python\nX.head()\n```\n输出结果：\n\n![在这里插入图片描述](/img/learn/intro-to-machine-learning/3.2.png)\n\n使用这些命令可视化地检查数据是数据科学家工作的一个重要部分，你会经常在其中发现值得进一步研究的惊喜。\n\n---\n## 4、构建模型\n\n你将使用`scikit-learn`库创建模型。编写代码时，这个库被编写为`sklearn`，你将在示例代码中看到。`Scikit-learn` 很容易将处理存储在`DataFrames`中的数据进行建模，是最流行的库。\n\n建立和使用模型的步骤如下：\n\n`定义`: 它将是什么类型的模型？一个决策树吗？还是其他类型的模型？也指定了模型类型的一些参数。\n`拟合: `从提供的数据中捕获模式，这是建模的核心。\n`预测: `表面意思，这个就不说了\n`评估: `确定模型的预测有多准确。\n\n下面是一个用`scikit-learn`定义一个`决策树模型`，并用`特性`和`目标`变量进行拟合的例子：\n```python\nfrom sklearn.tree import DecisionTreeRegressor\n\n# 定义模型. 指定一个参数random_state确保每次运行结果一致\nmelbourne_model = DecisionTreeRegressor(random_state=1)\n\n# 拟合模型\nmelbourne_model.fit(X, y)\n```\n输出结果：\n```\nDecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n                      min_impurity_split=None, min_samples_leaf=1,\n                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n                      presort=False, random_state=1, splitter='best')\n```\n许多机器学习模型在模型训练中允许一定的随机性。为`random_state`指定一个数字可以确保每次运行得到相同的结果。这被认为是一种很好的做法。你使用任何数字，而模型的质量并不完全取决于你所选择的值。\n\n我们现在有了一个拟合的模型，可以用来进行预测。\n\n在实践中，你会想要预测即将上市的新房子，而不是我们已经有价格的房子。但我们将对训练数据的前几行进行预测，以了解`predict`函数是如何工作的。\n```python\nprint(\"Making predictions for the following 5 houses:\")\nprint(X.head())\nprint(\"The predictions are\")\nprint(melbourne_model.predict(X.head()))\n```\n输出结果：\n```\nMaking predictions for the following 5 houses:\n   Rooms  Bathroom  Landsize  Lattitude  Longtitude\n1      2       1.0     156.0   -37.8079    144.9934\n2      3       2.0     134.0   -37.8093    144.9944\n4      4       1.0     120.0   -37.8072    144.9941\n6      3       2.0     245.0   -37.8024    144.9993\n7      2       1.0     256.0   -37.8060    144.9954\nThe predictions are\n[1035000. 1465000. 1600000. 1876000. 1636000.]\n```\n## 5、去吧，皮卡丘\n在[模型构建练习](https://www.kaggle.com/kernels/fork/400771)中自己尝试一下~\n\n"
  },
  {
    "path": "docs/Kaggle/learn/intro-to-machine-learning/4.md",
    "content": "# Kaggle 官方教程：机器学习入门4 模型验证\n> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [Model Validation](https://www.kaggle.com/dansbecker/model-validation)\n> \n> 译者：[Leytton](https://github.com/Leytton)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPS：水平有限，欢迎交流指正（Leytton@126.com）\n\n你已经建立了一个模型。但是它好不好呢?\n在本节课中，你将学习使用模型验证来度量模型的质量。度量模型质量是迭代改进模型的关键。\n\n## 1、什么是模型验证\n你将需要评估几乎所有构建的模型。在大多数应用程序中，模型质量的相关度量是预测精度。换言之，模型预测结果是否接近实际发生情况。\n\n`许多人在测量预测精度时犯了一个巨大的错误。他们用训练数据进行预测，并将预测结果与训练数据中的目标值进行比较。`你很快就会发现这个弊端并且学习如何解决它，先让我们想一下该怎么做吧。\n\n你首先需要以一种可理解的方式总结模型质量。如果你比较10000套房子的预测和实际房价，你可能会发现好坏参半。浏览10000个预测值和实际值的数据是没有意义的。我们需要将其总结为一个单一的度量。\n\n总结模型质量有许多度量标准，但我们将从一个称为`平均绝对误差(Mean Absolute Error，也称为MAE)`的度量标准开始。让我们从最后一个单词`error`开始分析这个度量。\n\n每栋房子的预测误差为:\n```\nerror = actual − predicted\n```\n所以，如果一栋房子实际售价15万美元，而你预测它的售价是10万美元，则误差是5万美元。\n\n利用MAE度量，我们取每个误差的绝对值，这将把每个误差转换为一个正数。然后取绝对误差的平均值。这就是我们对模型质量的评估。也可以这么说：\n>我们的预测平均偏离了X。\n\n为了计算MAE，我们首先需要一个模型：\n\n```python\nimport pandas as pd\n\n# 加载数据\nmelbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'\nmelbourne_data = pd.read_csv(melbourne_file_path) \n# 过滤缺失数据\nfiltered_melbourne_data = melbourne_data.dropna(axis=0)\n# 选择预测目标和特征\ny = filtered_melbourne_data.Price\nmelbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'BuildingArea', \n                        'YearBuilt', 'Lattitude', 'Longtitude']\nX = filtered_melbourne_data[melbourne_features]\n\nfrom sklearn.tree import DecisionTreeRegressor\n# 定义模型\nmelbourne_model = DecisionTreeRegressor()\n# 拟合模型\nmelbourne_model.fit(X, y)\n```\n我们有了一个模型，下面来计算平均绝对误差：\n```python\nfrom sklearn.metrics import mean_absolute_error\n\npredicted_home_prices = melbourne_model.predict(X)\nmean_absolute_error(y, predicted_home_prices)\n```\n输出数据：\n```\n434.71594577146544\n```\n## 2、“样本内”分数问题\n刚刚计算的测量值可以称为“样本内”分数。我们使用了单个的房屋“样本”来构建模型并对其进行评估。这就是弊端产生原因。\n\n想象一下，在大型房地产市场中，门的颜色与房价无关。\n\n然而，在你用于构建模型的数据样本中，所有带有绿色门的住宅都非常昂贵。该模型的工作是找到预测房价的模式，所以它会看到这种模式，而且总是会将带有绿色门的房子预测为高价。\n\n由于这种模式是从训练数据中推导出来的，所以模型在训练数据中会显得比较准确。\n但是，如果模型使用新数据进行预测，这种模式是不成立的，在实际使用中，模型将非常不准确。\n\n由于模型的实用价值体现在对新数据的预测，所以我们需要使用没有参与模型构建的数据，来度量模型性能。\n最直接的方法是从模型构建过程中`排除一些数据`，然后使用这些数据来测试模型的准确性。这些数据称为`验证数据`。\n\n## 3、编程实现\n`scikit-learn`库有一个函数`train_test_split`，用于将数据分成两部分。我们将使用一部分数据作为训练数据来适应模型，并将使用另一部分数据作为验证数据来计算`mean_absolute_error`。\n\n代码如下：\n```python\nfrom sklearn.model_selection import train_test_split\n\n# 将数据分割为训练和验证数据，都有特征和预测目标值\n# 分割基于随机数生成器。为random_state参数提供一个数值可以保证每次得到相同的分割\n# 执行下面代码\ntrain_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)\n# 定义模型\nmelbourne_model = DecisionTreeRegressor()\n# 拟合模型\nmelbourne_model.fit(train_X, train_y)\n\n# 根据验证数据获得预测价格\nval_predictions = melbourne_model.predict(val_X)\nprint(mean_absolute_error(val_y, val_predictions))\n```\n输出数据：\n```\n260585.51323434475\n```\n\n## 3、哇！\n\n样本内数据的平均绝对误差是500美元。样本外价格超过25万美元。\n\n这就是一个几乎完全正确的模型和一个不实用模型之间的区别。实际上，验证数据中的平均房屋价值为110万美元。因此，预测误差约为实际平均房价的四分之一。\n\n有很多方法可以改进这个模型，比如尝试找到更好的特征或使用不同类型的模型。\n\n## 4、去吧，皮卡丘\n在我们考虑改进这个模型之前，请自己尝试[模型验证](https://www.kaggle.com/kernels/fork/1259097)。\n\n原文：\nhttps://www.kaggle.com/dansbecker/model-validation"
  },
  {
    "path": "docs/Kaggle/learn/intro-to-machine-learning/5.md",
    "content": "# Kaggle 官方教程：机器学习入门5 欠拟合与过拟合\n> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [Underfitting and Overfitting](https://www.kaggle.com/dansbecker/underfitting-and-overfitting)\n> \n> 译者：[Leytton](https://github.com/Leytton)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPS：水平有限，欢迎交流指正（Leytton@126.com）\n\n在这一步的最后，你将了解欠拟合和过拟合的概念，并将能够应用这些概念使你的模型更加准确。\n\n## 1、尝试不同的模型\n\n现在你已经有了一种可靠的方法来度量模型的准确性，你可以使用其他模型进行试验，看看哪个模型的预测效果最好。那么有哪些模型可选择呢?\n\n你可以在`scikit-learn`的文档中看到，决策树模型有许多选项。最重要的选项决定了树的深度。回想一下这门微课程的第一节课，一棵树的深度是它在做出预测之前进行分裂次数的度量。这是一棵相对较浅的树：\n\n![在这里插入图片描述](/img/learn/intro-to-machine-learning/5.1.png)\n\n在实践中，一棵树的顶层(所有的房子)和一片叶子之间有10个分叉是很常见的。随着树越来越深，数据集被分割成具有更少房子的叶子。\n\n如果一棵树只有一个分叉，它将数据分成两组。如果每组再分裂一次，我们会得到4组房子。再把它们分成8组。如果我们在每一层增加更多的分叉，使群组数量翻倍，到第10层时，我们将有2^10组房子。即1024片叶子。\n\n当我们把房子分成许多片叶子时，每片叶子上的房子也就更少了。叶子上的房子越少，则预测值更接近房子的实际价值。但它们对新数据的预测可能非常不可靠(因为每个预测都只基于少数房子)。\n\n这种现象叫做`过拟合`，模型与训练数据几乎完全匹配，但在验证其他新数据方面效果很差。另一方面，如果我们设计的树很浅，它不会把房子分成很明显的组。\n\n在极端情况下，如果一棵树只有2或4个分支，则各个叶子仍然有各种各样的房子。这就导致预测房价相差甚远，即使是在训练数据中(由于这个原因，验证结果也会很糟糕)。当一个模型不能捕捉到数据中的重要特征和模式时，它在训练数据时就表现得很差，这称为`欠拟合`。\n\n由于我们关心新数据的准确性，根据验证数据估算，我们希望在`欠拟合`和`过拟合`之间找到一个最佳点。在视觉上，我们想要(红色)验证曲线的最低点。\n\n![在这里插入图片描述](/img/learn/intro-to-machine-learning/5.2.png)\n\n## 2、案例\n有几种方法可以控制树的深度，树的一些路径可以比其他路径有更大的深度。但是`max_leaf_nodes`参数提供了一种非常合适的方法来控制`过拟合`和`欠拟合`。我们允许模型生成的叶子越多，在上图中就越接近`过拟合区域`。\n\n我们可以使用一个实用函数来比较不同`max_leaf_nodes`值模型的`MAE分数`：\n```python\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.tree import DecisionTreeRegressor\n\ndef get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y):\n    model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)\n    model.fit(train_X, train_y)\n    preds_val = model.predict(val_X)\n    mae = mean_absolute_error(val_y, preds_val)\n    return(mae)\n```\n数据被加载进`train_X`, `val_X`, `train_y` 和 `val_y` 变量：\n```python\nimport pandas as pd\n    \n# 加载数据\nmelbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'\nmelbourne_data = pd.read_csv(melbourne_file_path) \n# 过滤缺失数据\nfiltered_melbourne_data = melbourne_data.dropna(axis=0)\n# 选择特征和目标值\ny = filtered_melbourne_data.Price\nmelbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'BuildingArea', \n                        'YearBuilt', 'Lattitude', 'Longtitude']\nX = filtered_melbourne_data[melbourne_features]\n\nfrom sklearn.model_selection import train_test_split\n\n# 将数据分割为训练和验证数据，都有特征和预测目标值\ntrain_X, val_X, train_y, val_y = train_test_split(X, y,random_state = 0)\n```\n\n我们可以使用`for循环`来比较使用不同`max_leaf_nodes`值构建模型的准确性。\n```python\n# 比较不同max_leaf_nodes值的MAE\nfor max_leaf_nodes in [5, 50, 500, 5000]:\n    my_mae = get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y)\n    print(\"Max leaf nodes: %d  \\t\\t Mean Absolute Error:  %d\" %(max_leaf_nodes, my_mae))\n```\n输出数据：\n```\nMax leaf nodes: 5  \t\t Mean Absolute Error:  347380\nMax leaf nodes: 50  \t\t Mean Absolute Error:  258171\nMax leaf nodes: 500  \t\t Mean Absolute Error:  243495\nMax leaf nodes: 5000  \t\t Mean Absolute Error:  254983\n```\n在列出的选项中，500个是最佳的叶子数。\n\n## 3、结论\n\n结论是，构建模型可能遇到这两种情况：\n- `过拟合`： 捕捉那些在未来不会重现的虚假模式，导致预测不那么准确。\n- `欠拟合`： 未能捕捉到相关的模式，导致预测不那么准确。\n\n我们使用不参与模型训练的验证数据来度量候选模型的准确性。这让我们可以尝试多种候选模型后，保留最佳模型。\n\n## 4、去吧，皮卡丘\n尝试[优化你之前构建的模型](https://www.kaggle.com/kernels/fork/1259126)\n"
  },
  {
    "path": "docs/Kaggle/learn/intro-to-machine-learning/6.md",
    "content": "# Kaggle 官方教程：机器学习入门6 随机森林\n> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [Random Forests](https://www.kaggle.com/dansbecker/random-forests)\n> \n> 译者：[Leytton](https://github.com/Leytton)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPS：水平有限，欢迎交流指正（Leytton@126.com）\n\n## 1、介绍\n`决策树`给你留下一个难题。一颗较深、叶子多的树将会`过拟合`，因为每一个预测都来自叶子上仅有的几个历史训练数据。一颗较浅、叶子少的树将会`欠拟合`，因为它不能在原始数据中捕捉到那么多的差异。\n\n即使是当今最精良的建模技术，也面临着拟合不足和拟合过度之间问题。但是，许多模型通过一些不错的点子来提升效果。我们将以`随机森林`为例。\n\n`随机森林`使用了许多树，它通过对每棵成分树的预测进行平均来进行预测。它通常比单个决策树具有更好的预测精度，并且使用默认参数效果也不错。如果继续建模，你可以学习更多具有更好性能的模型，但是其中许多模型效果受调参影响特别大。\n\n## 2、案例\n你已经多次看到加载数据的代码。数据加载后，我们将得到以下变量：\n - train_X\n - val_X\n -  train_y\n -  val_y\n\n```python\nimport pandas as pd \n# 加载数据\nmelbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'\nmelbourne_data = pd.read_csv(melbourne_file_path) \n# 过滤缺失数据\nmelbourne_data = melbourne_data.dropna(axis=0)\n# Choose target and features\ny = melbourne_data.Price\nmelbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'BuildingArea', \n                        'YearBuilt', 'Lattitude', 'Longtitude']\nX = melbourne_data[melbourne_features]\n\nfrom sklearn.model_selection import train_test_split\n\n# 将数据分割为训练和验证数据，都有特征和预测目标值\n# 分割基于随机数生成器。为random_state参数提供一个数值可以保证每次得到相同的分割\n# 执行下面代码\ntrain_X, val_X, train_y, val_y = train_test_split(X, y,random_state = 0)\n```\n\n我们构建了一个随机森林模型，类似于我们在scikit-learn中构建决策树的方法——这次使用的是`RandomForestRegressor`类，而不是`DecisionTreeRegressor`。\n\n```python\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_absolute_error\n\nforest_model = RandomForestRegressor(random_state=1)\nforest_model.fit(train_X, train_y)\nmelb_preds = forest_model.predict(val_X)\nprint(mean_absolute_error(val_y, melb_preds))\n```\n输出结果：\n```\n202888.18157951365\n\n/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n```\n## 3、结论\n可能还有进一步改进的空间，但是这比最佳决策树250,000的误差有很大的改进。你可以修改一些参数来提升随机森林的性能，就像我们改变单个决策树的最大深度一样。但是，随机森林模型的最佳特性之一是，即使没有这种调优，它们通常也可以正常工作。\n\n你很快就会了解`XGBoost`模型，它在使用正确的参数进行调优时提供了更好的性能(但是需要一些技巧才能获得正确的模型参数)。\n\n## 4、去吧，皮卡丘\n尝试自己[使用一个随机森林模型](https://www.kaggle.com/kernels/fork/1259186)，看看它对你的模型有多大的改进。\n\n"
  },
  {
    "path": "docs/Kaggle/learn/intro-to-machine-learning/7.md",
    "content": "# Kaggle 官方教程：机器学习入门7 继续你的征程\n> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [Exercise: Machine Learning Competitions](https://www.kaggle.com/kernels/fork/1259198)\n> \n> 译者：[Leytton](https://github.com/Leytton)\n> \n> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPS：水平有限，欢迎交流指正（Leytton@126.com）\n\n《Kaggle教程 机器学习入门》系列课程翻译完毕，撒花 ✿✿ヽ(°▽°)ノ✿\n\n## 1、机器学习竞赛\n进入机器学习竞赛的世界，不断提高，看看你的进步。\n[https://www.kaggle.com/kernels/fork/1259198](https://www.kaggle.com/kernels/fork/1259198)\n\n## 2、继续你的征程\n\n有很多方法可以改进你的模型，此时，`尝试是一个很好的学习方法`。.\n\n改进模型的最佳方法是添加特征。看看这些数据列表，想想什么可能影响房价。缺失值或非数字数据类型将导致错误。\n\n[《中级机器学习》](https://www.kaggle.com/learn/intermediate-machine-learning)微课程将教你如何处理这些类型的特征。你还将学习使用`xgboost`，这是一种比`Random Forest`更精确的技术。\n\n## 3、其他微课程\n\n[《Pandas》](https://kaggle.com/Learn/Pandas) 微课程将为你讲解数据操作技巧，使你能够快速地实现从概念到数据科学项目的实现。\n\n[《深度学习》](https://kaggle.com/Learn/Deep-Learning)微课程中，建立模型在计算机视觉任务中表现得比人类更好。"
  },
  {
    "path": "docs/kaggle-quickstart.md",
    "content": "# [Kaggle](https://www.kaggle.com) 入门操作指南\n\n## [注册](https://www.kaggle.com/?login=true)\n\n1. 首先注册账号\n2. 关联 GitHub 账号\n\n![](/img/docs/login.jpg)\n\n## [竞赛 - competitions](https://www.kaggle.com/competitions)\n\n* [选择 - All 和 Getting Started](https://www.kaggle.com/competitions?sortBy=deadline&group=all&page=1&pageSize=20&segment=gettingStarted)\n\n![](/img/docs/All-GettingStarted.jpg)\n\n* [选择 - Digit Recognizer（数字识别器）](https://www.kaggle.com/c/digit-recognizer)\n\n![](/img/docs/choose-digit-recognizer.jpg)\n\n* [阅读资料 - Digit Recognizer（数字识别器）](https://www.kaggle.com/c/digit-recognizer)\n\n**选择 版本框架 和 star 最高的 Kernels 编辑就行，然后模仿 [**数字识别**](/competitions/getting-started/digit-recognizer) 案例更新**\n\n![](/img/docs/read-digit-recognizer.jpg)\n\n## 项目规范(以：DigitRecognizer 为例)\n\n> 文档：结尾文件名为项目名.md\n\n* 案例：`competitions/getting-started/digit-recognizer.md`\n* 例如：数字识别，文档是属于 `competitions -> GettingStarted` 下面，所以创建 `competitions/getting-started` 存放文档就行\n\n> 图片：结尾文件名可自由定义\n\n* 案例：`static/images/comprtitions/getting-started/digit-recognizer/front_page.png`\n* 例如：数字识别，图片是属于 `competitions -> GettingStarted -> DigitRecognizer` 下面，所以创建 `competitions/getting-started/digit-recognizer` 存放文档的图片就行\n\n\n> 代码：结尾文件名可自由定义.py\n\n* 案例：`src/py3.x/kaggle/getting-started/digit-recognizer/dr_knn_pandas.py`\n* 例如：数字识别，代码只有 `竞赛` 有，所以直接创建 `getting-started/digit-recognizer` 存放代码就行\n* 要求（方法：完全解耦）\n    1. 加载数据\n    2. 预处理数据(可没)\n    3. 训练模型\n    4. 评估模型(可没)\n    5. 导出数据\n* 标注python和编码格式\n\n```python\n#!/usr/bin/python\n# coding: utf-8\n```\n\n*  标注项目的描述\n\n```python\n'''\nCreated on 2017-10-26\nUpdate  on 2017-10-26\nAuthor: 【如果是个人】片刻\nTeam:   【如果是团队】装逼从不退缩（张三、李四 等等）\nGithub: https://github.com/apachecn/kaggle\n'''\n```\n\n> 数据：结尾文件名可自由定义\n\n* 输入：`datasets/getting-started/digit-recognizer/input/train.csv`\n* 输出：`datasets/getting-started/digit-recognizer/ouput/Result_sklearn_knn.csv`\n* 例如：数字识别，数据只有 `竞赛` 有，所以直接创建 `getting-started/digit-recognizer` 存放数据就行\n\n> 结果提交\n\n将数据的输出结果提交到项目的页面中\n\n<a href=\"https://www.kaggle.com/c/digit-recognizer/submit\" target=\"_blank\">\n<img src=\"/img/docs/kaggle-submit.jpg\">\n</a>\n\n## docs目录（可忽略）\n\n`docs 目录存放的是 ApacheCN 整理的操作or说明文档，和 kaggle 网站内容没有关系`\n\n**后面会持续更新**\n"
  },
  {
    "path": "docs/kaggle-start.md",
    "content": "# kaggle 组队开始啦\n\n## 1、前言\n\n我们学习 ML 的知识已经有一段时间了。不知道大家的 ML 技能还有实际问题的处理能力是不是一天一天在增长，也不知道大家是否找到了一个适合自己突破的学习方式。正所谓，学如逆水行舟，不进则退。既然我们希望增长得更快，况且我们身边有这么多的资源（不利用岂不是可惜），这样一想，这不是正好让我们展示一下自己的技术（是时候表演真正的技术了）？\n\n装逼的江湖里可能不会出现李淳罡，但是谁能保证你不是下一个徐凤年呢？\n\n于你而言，阿里天池、京东金融大赛、DF、CCF、kaggle 比赛，这不就是一个个江湖吗？\n\n难道你就不想一声 “剑来！” 令那数千飞剑遮天蔽日？难道你就不想为那一袭红衣剑开天门？难道你就不想 “天上剑仙三百万，遇我也须尽低眉” ？还有比这更装逼的事情？\n\n## 2、正文\n\n考虑到大家学习完成 ML 的基础知识之后，可能会苦于一身本事无处施展，更准确来说，是学完之后没有练手的机会。所以，ApacheCN 准备开始组织大家一起刷 kaggle 啦啦啦~~~\n\n### 2.1、运筹帷幄\n\n[片刻](https://github.com/jiangzhonglian) 大佬已经在 github 中写了一个简单的小例子 ---> [数字识别](https://github.com/apachecn/kaggle/blob/master/competitions/getting-started/digit-recognizer.md)，供大家来参考。\n\n近期因为涉及到 ApacheCN 的组织架构的调整，还有 sklearn 0.19.X 项目的进行等外在因素，可能片刻大佬没有时间和大家一起，我们对这次活动进行了一个简单的小规划，请看下面~~~\n\n* 初期规划 1-3 个人一队，当然也可以调整，小队人数方面比较自由（为你们可以在群里尽情的搞基提供保障）\n* 建议先从简单的小比赛入手，比如 [手写数字识别](https://www.kaggle.com/c/digit-recognizer) 、 [预测房价](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) 、 [泰坦尼克](https://www.kaggle.com/c/titanic)\n* 初期规划每个小比赛 1-2 周每个小比赛之后有一次 ApacheCN 各参赛小队排名，根据 leaderboard 上面的最高成绩排名\n* 排名前 3 的小队给大家做一下直播，讲解一下获得好成绩的心路历程\n* 提交到 github 上，接受来自大家的膜拜~~~\n\n### 2.2、初入江湖\n\n在正式开始之前，你需要了解一下相关的背景知识\n\n* github 地址： https://github.com/apachecn/kaggle\n* 阅读一下 [README.md](https://github.com/apachecn/kaggle) 文档，了解一下套路\n* 阅读一下 docs 目录下的文档（ [github 操作](https://github.com/apachecn/kaggle/blob/master/docs/GitHub/README.md) 和 [kaggle 操作](https://github.com/apachecn/kaggle/blob/master/docs/kaggle-quickstart.md)），熟悉一下具体的操作\n\n### 2.3、小有名气\n\n了解完一些相关的小套路之后，为自己定一下位。\n\n* 如果你已经是大佬了，那就不需要从头开始了，直接找与你水平相近的人开始组队吧，直接上有难度的比赛；\n* 如果你和我一样是个萌新，那就找一些和你一样的或水平比你高而且愿意带你的人组队，开始征战之旅吧！\n\n少侠，无论做啥最幸运的就是碰壁或遇到挫折，跌宕起伏才是人生，如果一帆风顺，这样的人生我宁愿不要！  \n最可怕的是不敢，连自己这一关都过不去，你还想成为大佬？别做梦了！大不了重新来过，因为......你还年轻。年少人做年少事，有担当之事等到而立之年再去做。\n\n如果不知道从哪儿开始，那就从 [手写数字识别](https://www.kaggle.com/c/digit-recognizer) 开始吧。\n\n### 2.4、名动一方\n\n组队，并不意味着你变得轻松了，相反，你可能会变得更累，事情更多。还有千万不要把自己想成一个可有可无的人，这样的想法真的很让人害怕。在一个小队里面，问题的解决就靠大家的相互交流，加倍信任，好成绩的获得，一定离不开超级棒的团队合作。\n\n* 组队之后，各小组可以私下里建个讨论组一起讨论问题，但是更建议大家直接在群里提问，群里解答，毕竟群里卧虎藏龙，我能说，咱们群里有好几个大佬吗？他们玩比赛拿奖都拿到手软，但是我不能透露他的名字~~~\n\n* 在群里，大家尽情头脑风暴，也正是因为这样的跳跃思维，才会出现更加好的问题解决方式，才会出现 leak 般的答案。\n\n* 当然，组队完成之后，在群里你可能会发现某个人，或者某几个人在做比赛的时候思维炒鸡活跃，得到的结果也相当 Nice ，那还等什么，这是大腿啊！！！还不赶紧抱紧了。\n\n### 2.5、天下闻名\n\n等到每个小比赛结束之后，大家把自己相应的代码提交到 [github](https://github.com/apachecn/kaggle) 中，以小队的名称命名文件夹，接收来自世界各地的膜拜吧。唔哈哈哈哈哈哈哈哈。。。当然如果小队人员不愿意将自己的核心代码开源出来，我们也会尊重你们小队的意见~~~\n\n* 排名前 3 的小队，选出负责人或者团队成员一起，对如何获得这样一个令（rang）人（ren）害（jiao）怕（ao）的成绩做一下总结，这才是我们想要的干货（就是这么傲娇）。当然最好的结果是让其他人能够从你们的经历中学到一些经验，比如：数据预处理需要注意的地方，建模完成后怎么进行验证等等。\n\n* 我们尽量是以 **文档+代码+视频** 的形式或其中之一的形式开源出来，不因为别的，只因为我们是 ApacheCN 。\n\n* 每个参加比赛的小队都有积分可以拿，但是积分多少需要看成绩来定啦，^_^，成绩越好，积分越高啊~~~\n\n### 2.6、一代宗师\n\n就这样驰骋在 kaggle 的沙场上，只要你没有被比赛压垮，你就能把比赛压垮。\n\n咱们 ApacheCN 打算用 kaggle 上的中小型比赛让大家练手，但是手热了，我们该怎么办呢？咱们还有下面的几个选择：\n\n* 继续挑战 kaggle 上的高难度比赛，一天不拿到 kaggle 的 Grandmaster 称号，一天就不告老还乡。\n\n* 转战国内的比赛 ===> 天池/京东/CCF/challenger.ai ，沙场真的是应有尽有，就看你这千里马中意哪片草原了。\n\n到时候，咱们 ApacheCN 内部会进行一次大讨论，定一下未来的走向。\n\n### 2.7、超凡入圣\n\n廉颇老矣，尚能饭否？\n\n各大比赛你心里基本都有一点自己的认知了，自己也有了很大的成长了，自己的技术达到什么水平你心里也是门清儿，所以....你要停下来思考清楚自己接下来的路怎么走了！\n\n举个栗子：\n\n* 喜欢各种算法/建模 ===> 算法工程师/数据科学家/自动驾驶工程师\n* 喜欢数据的处理：清洗/沉淀/分析等  ===> 数据开发工程师/大数据开发工程师\n* 不喜欢数据方面的流程，还是喜欢功能模块开发 ===> web工程师\n\n无论怎么样，到这个阶段，你已经知道自己接下来的路该怎么走了，不是吗？\n\n### 2.8、天外飞仙\n\n大佬，以后的路就只能靠你们自己走咯，哈哈哈哈~\n\n还请萌新在咨询你们如何入门的时候，为他们指明一条可以走下去的路...\n\n## 3、最后的寄语\n\n我的心中是一直有一个江湖的……那里的山，云雾缭绕；那里的水，波光粼粼；那里的将士，持三尺剑立不世功；那里的侠客，十步杀一人千里不留行；那里的姑娘，美得万人空巷。那里有快马厮杀的豪气，也有一剑斩六合的孤胆英雄，只因那里的名字叫江湖。\n\n江湖很美，一人一马就能踏遍满山桃花，有不世武功便可仗剑天涯，侠士们都是剑眉星目，姑娘们都烨若桃花。\n\n江湖卧虎藏龙，你走进一家客栈，匾额上书两字：月来！\n\n乍一看，不得了啊，这客栈里都不是一般人哇，你看，那坐正中间的大汉，身长九尺，髯长二尺，不怒自威；你再看那边另一位兄台，豹头环眼，燕颔虎须，势如奔马。你可能会感叹，长相便如此磅礴，服气！大哥，干了这碗酒，都在酒里了。别着急喝呀，这些可都是小喽啰，厉害的人，可在后头呢！你看那窗边，默默沏茶的黑衣男子，是不是像一棵不倒的青松？你看那炉边轻若芙蕖的酒女，是否袖中偶尔有寒光闪过？你看那低头哈腰的店小二，为何行走时听不到丝毫脚步声？\n\n江湖时而精彩，却又偶现残缺。那位公子远赴边城，姑娘十八里相送，为什么眼睛红了呢？平日里威风凛凛的刀客，为何神色寂寥地坐在窗边？是否在想错手杀死的那个姑娘？宫斗剧里风姿绰约的姑娘们，都在为了皇帝的青睐和后位的宝座勾心斗角，左右使绊子。青春剧里的男生女生，不知又包了几座鱼塘，看了几夜流星。美剧里的时而颓废时而振作的男主女主们，不知又拯救了多少次世界。\n\n但是江湖，还是那样，侠客们大口吃肉大碗喝酒，饮尽了一夜的明月，散尽了一宿的恩仇。那里的姑娘，身骑白马走三关，眼睛始终清澈得像泸沽湖的水，神采飞扬像三月的桃花……那里没有利欲熏心，绝不会背信弃义。话不投机，我就不屑言语，遇到知己，就倾杯相交，他们始终保持着一身风骨，不负权威，不畏权贵，不贪权力。你若是诧异，他们就飒然一笑，说，小爷我可是江湖人！江湖人自有江湖人的风骨，江湖自有江湖的不凡，江湖包罗万象又精彩纷呈，任你贪癫痴怨又六根不净，这江湖，都欢迎你。罢了，看你如此向往，少侠我便陪你喝一杯，跟你讲讲这人生到底该怎么过才爽利！\n\n干了这碗酒，你我都是江湖人！\n\n最后附上我超级喜欢的一句话：\n\n* 李淳罡愿世间心诚剑士人人会两袖青蛇。 \n* 李淳罡愿天下惊艳后辈人人可剑开天门。\n"
  },
  {
    "path": "docs/writeup-list.md",
    "content": "# 解决方案列表\n\n## 结构化数据/时间序列\n\n1.  2018科大讯飞AI营销算法大赛\n\n    Rank1：https://zhuanlan.zhihu.com/p/47807544\n\n2.  2018 IJCAI 阿里妈妈搜索广告转化预测\n\n    Rank1：https://github.com/plantsgo/ijcai-2018 \n\n    Rank2：\n    \n    +   https://github.com/YouChouNoBB/ijcai-18-top2-single-mole-solution \n\n    +   https://blog.csdn.net/Bryan__/article/details/80600189 \n\n    Rank3: https://github.com/luoda888/2018-IJCAI-top3 \n\n    Rank8: https://github.com/fanfanda/ijcai_2018 \n\n    Rank8: https://github.com/Gene20/IJCAI-18 \n\n    Rank9（第一赛季）https://github.com/yuxiaowww/IJCAI-18-TIANCHI \n\n    Rank29: https://github.com/bettenW/IJCAI18_Tianchi_Rank29 \n\n    Rank41: https://github.com/cmlaughing/IJCAI-18 \n\n    Rank48: https://github.com/YunaQiu/IJCAI-18alimama \n\n    Rank53: https://github.com/altmanWang/IJCAI-18-CVR \n\n    Rank60: https://github.com/Chenyaorui/ijcai_2018 \n\n    Rank81: https://github.com/wzp123456/IJCAI_18 \n\n    Rank94: https://github.com/Yangtze121/-IJCAI-18- \n\n3.  2018腾讯广告算法大赛\n\n    Rank3: https://github.com/DiligentPanda/Tencent_Ads_Algo_2018 \n\n    rank6: https://github.com/nzc/tencent-contest \n\n    Rank7: https://github.com/guoday/Tencent2018_Lookalike_Rank7th \n\n    Rank9: https://github.com/ouwenjie03/tencent-ad-game \n\n    Rank10: https://github.com/keyunluo/Tencent2018_Lookalike_Rank10th \n\n    rank10（初赛）: https://github.com/ShawnyXiao/2018-Tencent-Lookalike \n\n    Rank11: \n    \n    +   https://github.com/liupengsay/2018-Tencent-social-advertising-algorithm-contest \n\n    +   https://my.oschina.net/xtzggbmkk/blog/1865680 \n\n    Rank26: https://github.com/zsyandjyhouse/TencentAD_contest \n\n    Rank33: https://github.com/John-Yao/Tencent_Social_Ads2018 \n\n    Rank69: https://github.com/BladeCoda/Tencent2018_Final_Phrase_Presto \n\n4.  2018JDATA 用户购买时间预测\n\n    Rank9：https://zhuanlan.zhihu.com/p/45141799 \n    \n5.  2018 DF风机叶片开裂预警\n\n    Rank2：https://github.com/SY575/DF-Early-warning-of-the-wind-power-system \n    \n6.  2018 DF光伏发电量预测\n\n    Rank1：https://zhuanlan.zhihu.com/p/44755488?utm_source=qq&utm_medium=social&utm_oi=623925402599559168 \n\n    https://mp.weixin.qq.com/s/Yix0xVp2SiqaAcuS6Q049g \n           \n7.  AI全球挑战者大赛-违约用户风险预测\n\n    Rank1：https://github.com/chenkkkk/User-loan-risk-prediction \n    \n8.  2016融360-用户贷款风险预测\n\n    Rank7：https://github.com/hczheng/Rong360 \n    \n9.  2016 CCF-020优惠券使用预测\n\n    Rank1: https://github.com/wepe/O2O-Coupon-Usage-Forecast \n    \n10. 2016 ccf-农产品价格预测\n\n    Rank2: https://github.com/xing89qs/CCF_Product \n\n    Rank35: https://github.com/wqlin/ccf-price-prediction \n \n11. 2016 ccf-客户用电异常\n\n    Rank4: https://github.com/AbnerYang/2016CCF-StateGrid \n    \n12. 2016 ccf-搜狗的用户画像比赛\n\n    Rank1: https://github.com/hengchao0248/ccf2016_sougou \n\n    Rank3: https://github.com/AbnerYang/2016CCF-SouGou \n\n    Rank5: \n    \n    +   https://github.com/dhdsjy/2016_CCFsougou \n\n    +   https://github.com/dhdsjy/2016_CCFsougou2 \n\n    +   https://github.com/prozhuchen/2016CCF-sougou \n\n    +   https://github.com/coderSkyChen/2016CCF_BDCI_Sougou \n           \n13. 2016 ccf-联通的用户轨迹\n\n    RankX: https://github.com/xuguanggen/2016CCF-unicom \n    \n14. 2016 ccf-Human or Robots\n\n    Rank6: https://github.com/pickou/ccf_human_or_robot \n    \n15. 菜鸟-需求预测与分仓规划\n\n    Rank6:  https://github.com/wepe/CaiNiao-DemandForecast-StoragePlaning \n\n    Rank10: https://github.com/xing89qs/TianChi_CaiNiao_Season2 \n    \n## NLP\n\n1.  智能客服问题相似度算法设计——第三届魔镜杯大赛\n\n    rank6 https://github.com/qrfaction/paipaidai\n    \n    rank12 \n    \n    +   https://www.jianshu.com/p/827dd447daf9\n           \n    +   https://github.com/LittletreeZou/Question-Pairs-Matching\n\n\n## CV\n\n1. Kaggle-TGS\n\n   Rank1: http://t.cn/EzkDlOC   \n   Rank9: http://t.cn/EznzvYv  \n   Rank11:https://github.com/iasawseen/Kaggle-TGS-salt-solution\n   Rank14:https://github.com/lRomul/argus-tgs-salt\n   Rank15:https://github.com/adam9500370/Kaggle-TGS\n   Rank22: http://t.cn/EzYkR6i  \n   Rank56 https://github.com/Gary-Deeplearning/TGS-Salt\n\n2. Kaggle Google地标检索 \n\n   Rank1: http://t.cn/R1i7Xiy  \n   Rank14： http://t.cn/R1nQriY\n\n3. Lyft感知挑战赛   \n\n      赛题： http://t.cn/RBtrJcE  \n      Rank4： http://t.cn/RBtrMdw  \n      ​      http://t.cn/RBJnlug\n\n4. (Kaggle)CVPR 2018 WAD视频分割  \n\n   Rank2: http://t.cn/Ehp4Ggm\n\n5. Kaggle Google AI Open Images  \n\n    Rank15: http://t.cn/RF1jnis\n\n6. Quick, Draw! Kaggle Competition Starter Pack    \n   http://t.cn/EZAoZDM\n\n7. Kaggle植物幼苗图像分类挑战赛   \n\n   Rank1: http://t.cn/RBssjf6\n\n8. Kaggle Airbus Ship Detection Challenge (Kaggle卫星图像船舶检测比赛)   \n\n   Rank8: https://github.com/SeuTao/Kaggle_Airbus2018_8th_code\n   Rank21:https://github.com/pascal1129/kaggle_airbus_ship_detection\n\n\n9. kaggle RSNA Pneumonia Detection\n\n   Rank1:https://github.com/i-pan/kaggle-rsna18\n   Rank2:https://github.com/SeuTao/Kaggle_TGS2018_4th_solution\n   Rank3:https://github.com/pmcheng/rsna-pneumonia\n   Rank6:https://github.com/pfnet-research/pfneumonia\n   Rank10:https://github.com/alessonscap/rsna-challenge-2018\n   \n10. kaggle Carvana Image Masking Challenge\n\n   Rank1:https://github.com/asanakoy/kaggle_carvana_segmentation\n   Rank3:https://github.com/lyakaap/Kaggle-Carvana-3rd-place-solution\n\t\n11. kaggle Statoil/C-CORE Iceberg Classifier Challenge\n\n   Rank4: https://github.com/asydorchuk/kaggle/blob/master/statoil/README.md\n\t\n12. kaggle 2018 Data Science Bowl\n\t\n   Rank1: https://github.com/selimsef/dsb2018_topcoders\n   Rank2: https://github.com/jacobkie/2018DSB\n   Rank3: https://github.com/Lopezurrutia/DSB_2018\n   Rank4: https://github.com/pdima/kaggle_2018_data_science_bowl_solution\n   Rank5: https://github.com/mirzaevinom/data_science_bowl_2018\n    \n    \n## 经验文章\n\n1.  介绍featexp 一个帮助理解特征的工具包 http://www.sohu.com/a/273552971_129720\n\n2.  Ask Me Anything session with a Kaggle Grandmaster Vladimir I. Iglovikov \n    \n    PDF：https://pan.baidu.com/s/1XkFwko_YrI5TfjjIai7ONQ\n\n## 其它资源列表\n\n1.  数据比赛资讯：https://github.com/iphysresearch/DataSciComp\n\n2.  Kaggle top方案整理：https://github.com/EliotAndres/kaggle-past-solutions\n\n3.  [Data competition Top Solution 数据竞赛Top解决方案开源整理](https://github.com/Smilexuhc/Data-Competition-TopSolution)\n    \n## 大佬的Git\n\n1.  植物 ：https://github.com/plantsgo \n2.  wepon ：https://github.com/wepe \n3.  Snake：https://github.com/luoda888 \n4.  Drop-out：https://github.com/drop-out \n5.  金老师的知乎：https://zhuanlan.zhihu.com/jlbookworm \n6.  渣大：https://github.com/nzc \n7.  郭大：https://github.com/guoday \n    \n"
  },
  {
    "path": "docs/矩阵求导.md",
    "content": "# 矩阵求导公式\n\n![矩阵1](/img/math/矩阵1.jpg)"
  },
  {
    "path": "docs/面试求职/学历.md",
    "content": "# 【求职系列】人工智能学历真的重要吗？\n\n## 学历：奇怪现象？\n\n* 如果你是跨专业的学习？进入AI行业，别人会觉得你很励志很牛逼！\n* 但是如果你是低学历的人，企业一般认为你就是个垃圾（普遍而已）\n\n表现形式：\n\n1. 学历门槛（当然大家都有自己的理由，说降低面试成本 - 就是懒嘛！）\n2. 风险转嫁（如果你是高学历，即使被裁也就是看走眼；而学历低，就是眼界有问题）\n\n除非你做的最前沿的技术！\n\n* 因为最前沿，学历高的也没几个会。。你还得去教别人\n* 最后高学历的学会了，差不多也是该辞退你的时候，因为公司说你是没门面的事情。\n\n当然你还会问，那极个别的牛人成功了为什么社会没变化呢？\n\n* 因为人家当老板了，可能忘记了自己当初那么苦。\n* 毕竟他有那么多事情要做，干嘛非要帮助当初的自己呢？？？\n\n## 面试： 奇异现象？\n\n* 毕业季拼命的 刷题（leetcode、剑指 Offer）\n* 更有甚者，工作n年以后，面试还是刷题，刷题\n\n我不理解，持续刷题的意义是什么？\n\n```\n请问你工作用让你写这些东西吗？【没有，性能问题：数据库、分布式、GPU帮你解决】\n刷题为了获取精神上的愉悦？ 【考察你的逻辑思维？狗屁】\n    其实不然，这东西基本上不用都会忘记，这就是为什么每一次都要复习，背写！\n    我在5年的工作中，从大数据、算法策略、NLP，我都没有涉及到leetcode的题目\n    当然除非你重构一些框架，底层需要使用到一些复杂的数据结构【极少人】\n很多人从大学毕业就是这样过来的，所以他以为面试就这样，所以这样面试别人【沃日】\n```\n\n现在算法面试不管你什么谁，都要上来做个题目，我真想草你们这些人，真尼玛闲的蛋疼！\n\n* 你说一个应届生没啥社会经验，你考考，我理解\n* 但是人家有项目，有比赛经验，你还考数据结构，为什么这么闲呢？？\n* 我就想问你们，你们招聘人不就是使用开源框架做事情，难道还手写自己框架【沃日】\n\n## 公司：奇异现象？\n\n* 悲哀的是：\n* 不管毕业多久，很多人炫耀的资本还是学校\n* 而更悲哀的是：\n* 这些人也都习惯性的默认了这一点并继续炫耀\n……  社会现实到有些过时呢\n\n## 至少是个本科，是什么意思？\n\n* 首先，学历真的很重要。。。（至少是个本科）\n* 但是这个至少是个神马意思呢？？？\n\n就是说：\n\n* 如果一直没招到合适的研究生和博士，就勉为其难的试试本科，然后压低工资。。\n\n什么意思呢？\n\n* 就是说：你能力要强，工资还要低！（甚至可能还要给某些研究生or博士汇报工作）\n\n## 那么如果是专科呢，能学吗？\n\n专科当然也是有机会的，不过基本上即使技术过了也不会招聘你的！\n\n为什么？\n\n* 因为正常情况下大公司不需要学历低的人，无所谓你的能力。【即使叫你去面试】\n* 但是大公司的创业部门和中小型公司创业公司是需要有能力的人，所以机会还是有的。。\n\n那么我们该怎么办呢？\n\n\n调整好自己的心态，别总是觉得要去大公司什么的。\n\n很多从大公司出来的boss 创业都是直接带一个团队出来，要么就是从社招招聘。\n\n基本上也都是普通的大学生，只是公司大了就开始矫情，所以摆正好心态，开心学习就行\n\n记住一点：\n* 工资高\n* 圈子：例如 ApacheCN\n* 自学能力强、有自己的想法（知道自己喜欢什么、为什么学他就行）\n\n---\n\n## 最近有趣的新闻\n\n> 最近关于学历的残酷事实\n\n[AI泡沫崩了？抖音员工爆料：校招内推985本硕简历过不了！](https://mp.weixin.qq.com/s?__biz=MzUzODMxMTI5MA==&mid=2247485175&idx=1&sn=d5f2359685b02cf33594eceae1cf52ba&chksm=fad8e9e2cdaf60f4382b1dff40ab570f68d7b64f63f86a7d92701aaf2f26a9d6f50ce9d22916)\n\n本事件只是反映了一个问题，就是人已经过饱和。【饱和只是入门，高手依然很少】\n\n> 在国内，为什么我们看到的大佬都是高学历的？\n\n![](/img/面试求职/学历/学历1.jpg)\n\n话说：低学历别人也不会给你机会进入这个圈子？\n\n例如：\n\n* 求职这一块，求职各种刁难【高压力、低薪资】\n* 在社交圈这一块，基本上是无法进入他们的圈子【为什么大佬都要花钱进MBA什么圈】\n* 在融资这一块，你没学历背景基本上也融不到资\n* 学历这一块，是一个相互交流的点，【例如：你也是某某某学校的？我是你学弟！】\n* 国内人口流量过大，导致对人要求过于严格，甚至高于全球水平，甚至排名最高几名\n\n> 今日头条张一鸣炮轰公司HR: 按你们写的JD招聘，我自己都进不来公司\n\n现实和理想的差距 VS 老板的要求和HR行动\n\n![](/img/面试求职/学历/学历2.jpg)\n\n> 互联网-大公司人员的传播路径\n\n现在我们看到的现象是：看学历、看背景\n\n其实才是：个人能力、个人品质（其中还有各种小套路）\n\n头条CEO张一鸣虽然说了那些话，但其实改变是非常难的，因为公司已经被某些人腐蚀了！\n\n要改变就要大动干戈，掉血掉肉；要么就新建一个创业公司，重新找人，定义好文化规范。\n\n![](/img/面试求职/学历/学历3.jpg)\n\n解决方案：\n\n1. 其他的国家（相比而言：国外急缺这方面的人才）\n2. 提升自己的能力，但是短期是不可能实现的（所以这个建议是个废话，但是我想告诉有些人这是个现实问题）\n3. 选择换个方向，从算法转开发，说实话开发薪资并不低，并且你可以调用算法的接口，只是你叫开发工程师（别人叫算法工程师而已）【就好比找对象一样，1000个人（厉害不厉害都有）追一个妹子，所以作为一个男孩子而言，完全可以找一个其他的女孩，没必要在这个树上吊死。也不见得没有比她更好的，所以人得灵活一点】\n\n算法只是一种思想，我们学它，可以从事它的研究，也可以从事它的应用。\n\n一定别把自己搞死在这个地方就行。\n\n所以？你准备好了入坑吗？\n"
  },
  {
    "path": "docs/面试求职/简历.md",
    "content": "# 简历如何写？\n\n> 个人信息\n\n姓名、性别、学历、工龄、籍贯、现居地、电话、电子邮箱\n\n> 求职意向\n\n* 工作情况: 北京-全职\n* 期望职业: 数据/算法负责人\n* 期望月薪: 面议\n\n> 教育经历\n\n* 20xx.09-20xx.06    xxxxx大学    xxx     \n\n> 技能描述\n\n* 使用Python 6年、熟练使用 SQL\n* 熟悉主流算法: 分类、回归\n\n> 自我评价\n\n1. 工作踏实认真，负责过Kaggle比赛项目、企业反作弊、推荐和大数据项目\n2. 学习能力很强，热衷研究技术、经常分享交流技术、喜欢看Kaggle全球优秀kernels\n3. 人脉资源广泛，在GitHub和知乎有点小名气，坚持做了2年的机器学习的开源社区\n \n> 个人经历\n\n```\nxx    2018.09～现在       算法负责人  负责：负责AI算法实现和落地应用\nxx    2016.06～2018-08   社区负责人  负责： 负责整个AI社区的内容梳理\nxx    2014.08～2016.03   算法策略    负责：广告联盟反作弊算法策略\nxx    2013.11～2014.07   数据仓库    负责：数据仓库优化和可视化展现\n```\n\n> 项目经验\n\n* 工作时间：2018/09～现在\n* 所处职位：数据算法负责人\n* 日常工作\n    * 1/2/3/4 [聊工作相关的重点]\n\n> 自学经验\n\n* Pytorch + TensorFlow  (2017/06 ~ 现在)\n* 搜索：TensorFlow 2.0 - 教程: https://github.com/apachecn/AiLearning\n* Pytorch 没有是因为翻译了教程: https://github.com/apachecn/pytorch-doc-zh\n* 各种做过的开源项目\n"
  },
  {
    "path": "docs/面试求职/简历范文.md",
    "content": "# 简历范文\n\n## 自我评价模块\n\n![](img/自我评价范文.png)\n\n## 技能模块\n\n![](img/技能模块范文.png)\n\n## 项目模块\n\n![](img/项目模块范文1.png)\n\n![](img/项目模块范文2.png)\n"
  },
  {
    "path": "img/Algorithm/LeetCode/005/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/011/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/033/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/042/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/065/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/218/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/343/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/367/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/371/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/463/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/470/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/815/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/84/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/935/1.md",
    "content": "\n"
  },
  {
    "path": "img/Algorithm/LeetCode/README.md",
    "content": "# Images for the whole repository!\n"
  },
  {
    "path": "index.html",
    "content": "<!-- index.html -->\n\n<!DOCTYPE html>\n<html>\n<head>\n  <meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge,chrome=1\">\n  <meta name=\"viewport\" content=\"width=device-width,initial-scale=1\">\n  <meta charset=\"UTF-8\">\n  <meta name=\"referrer\" content=\"never\">\n  <link rel=\"stylesheet\" href=\"asset/vue.css\">\n  <link rel=\"stylesheet\" href=\"asset/style.css\">\n  <link rel=\"stylesheet\" href=\"asset/prism-darcula.css\">\n  <link rel=\"stylesheet\" href=\"asset/sidebar.min.css\">\n  \n  <!-- google ads -->\n  <script async src=\"//pagead2.googlesyndication.com/pagead/js/adsbygoogle.js\"></script>\n    \n  <!-- google webmaster -->\n  <meta name=\"google-site-verification\" content=\"pyo9N70ZWyh8JB43bIu633mhxesJ1IcwWCZlM3jUfFo\" />\n\n  <link rel=\"stylesheet\" href=\"asset/dark-mode.css\">\n  <script src=\"asset/dark-mode.js\"></script>\n  <link rel=\"stylesheet\" href=\"asset/share.css\">\n  <script src=\"asset/share.js\"></script>\n  <link rel=\"stylesheet\" href=\"asset/edit.css\">\n  <script src=\"asset/edit.js\"></script>\n  <link rel=\"stylesheet\" href=\"asset/back-to-top.css\">\n  <script src=\"asset/back-to-top.js\"></script>\n</head>\n<body>\n  <div id=\"app\">now loading...</div>\n  <script>\n    window.$docsify = {\n      loadSidebar: 'SUMMARY.md',\n      name: 'Interview',\n      auto2top: true,\n      themeColor: '#004eb7',\n      repo: 'apachecn/Interview',\n      plugins: [window.docsPlugin],\n      alias: {\n        '/.*/SUMMARY.md': '/SUMMARY.md',\n      },\n      bdStatId: '38525fdac4b5d4403900b943d4e7dd91',\n      cnzzId: '1275211409',\n      search: {\n        paths: 'auto',\n        placeholder: '搜索',\n        noData: '没有结果',\n      },\n      copyCode: {\n        buttonText: '复制',\n        errorText: 'Error',\n        successText: 'OK!',\n      },\n    }\n  </script>\n  \n  <script src=\"asset/docsify-katex.js\"></script>\n  <link rel=\"stylesheet\" href=\"asset/katex.min.css\"/>\n  <script src=\"asset/docsify.min.js\"></script>\n  <script src=\"asset/search.min.js\"></script>\n  <script src=\"asset/prism-java.min.js\"></script>\n  <script src=\"asset/prism-c.min.js\"></script>\n  <script src=\"asset/prism-cpp.min.js\"></script>\n  <script src=\"asset/prism-csharp.min.js\"></script>\n  <script src=\"asset/prism-javascript.min.js\"></script>\n  <script src=\"asset/prism-python.min.js\"></script>\n  <script src=\"asset/prism-php.min.js\"></script>\n  <script src=\"asset/docsify-copy-code.min.js\"></script>\n  <script src=\"asset/docsify-baidu-push.js\"></script>\n  <script src=\"asset/docsify-baidu-stat.js\"></script>\n  <script src=\"asset/docsify-cnzz.js\"></script>\n  <script src=\"asset/docsify-apachecn-footer.js\"></script>\n  <script src=\"asset/docsify-clicker.js\"></script>\n  <link rel=\"stylesheet\" href=\"asset/docsify-quick-page.css\">\n  <script src=\"asset/docsify-quick-page.js\"></script>\n  <script src=\"asset/docsify-sidebar-collapse.min.js\"></script>\n</body>\n</html>"
  },
  {
    "path": "old/.travis.yml",
    "content": "language: node_js # 构建所需的语言环境\nnode_js:\n  - \"v10.16.0\"  # 对应的版本\n\nbranches:\n  only:\n  - master    # 构建的分支\n\ncache:\n  directories:\n  - node_modules # 依赖缓存的目录\n\ninstall:\n - npm install -g gitbook-cli # 安装编译工具\n - gitbook fetch 3.2.3 # 安装 Gitbook 子版本\n\nscript:\n  - sh run_website.sh\n\nafter_script:\n  - cd _book\n  - git init\n  - git config user.name ${GH_UN}\n  - git config user.email ${GH_EMAIL}\n  - git add -A\n  - git commit -am \"$(date \"+%Y-%m-%d %H:%M:%S\")\"\n  - git push \"https://${GH_TOKEN}@github.com/${GH_USER}/${GH_REPO}.git\" master:${GH_BRANCH} -f\n\nenv:\n  global:\n    - GH_UN=jiangzhonglian\n    - GH_EMAIL=jiang-s@163.com\n    - GH_USER=apachecn\n    - GH_REPO=Interview\n    - GH_BRANCH=gh-pages\n"
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  {
    "path": "old/book.json",
    "content": "{\n    \"title\" : \"Interview 求职指南\",\n    \"author\" : \"ApacheCN\",\n    \"description\" : \"Interview 求职指南\",\n    \"language\" : \"zh-hans\",\n    \"plugins\": [\n        \"github\",\n        \"github-buttons\",\n        \"-sharing\", \n        \"insert-logo\",\n        \"sharing-plus\",\n        \"back-to-top-button\",\n        \"code\",\n        \"copy-code-button\",\n        \"pageview-count\",\n        \"edit-link\",\n        \"emphasize\",\n        \"alerts\",\n        \"auto-scroll-table\",\n        \"popup\",\n        \"hide-element\",\n        \"page-toc-button\",\n        \"tbfed-pagefooter\",\n        \"sitemap\",\n        \"advanced-emoji\",\n        \"expandable-chapters\",\n        \"splitter\",\n        \"search-pro\"\n    ],\n    \"pluginsConfig\": {\n        \"github\": {\n            \"url\": \"https://github.com/apachecn/Interview\"\n        },\n        \"github-buttons\": {\n            \"buttons\": [\n              {\n                \"user\": \"apachecn\",\n                \"repo\": \"Interview\", \n                \"type\": \"star\",\n                \"count\": true,\n                \"size\": \"small\"\n              }\n            ]\n        },\n        \"insert-logo\": {\n            \"url\": \"http://data.apachecn.org/img/logo.jpg\",\n            \"style\": \"background: none; max-height: 150px; min-height: 150px\"\n        },\n        \"hide-element\": {\n            \"elements\": [\".gitbook-link\"]\n        },\n        \"edit-link\": {\n            \"base\": \"https://github.com/apachecn/Interview/blob/master\",\n            \"label\": \"编辑本页\"\n        },\n        \"sharing\": {\n            \"qzone\": true,\n            \"weibo\": true,\n            \"twitter\": false,\n            \"facebook\": false,\n            \"google\": false,\n            \"qq\": false,\n            \"line\": false,\n            \"whatsapp\": false,\n            \"douban\": false,\n            \"all\": [\n                \"qq\", \"douban\", \"facebook\", \"google\", \"linkedin\", \"twitter\", \"weibo\", \"whatsapp\"\n            ]\n        },\n        \"page-toc-button\": {\n            \"maxTocDepth\": 4,\n            \"minTocSize\": 4\n        },\n        \"tbfed-pagefooter\": {\n            \"copyright\":\"Copyright &copy ibooker.org.cn 2019\",\n            \"modify_label\": \"该文件修订时间： \",\n            \"modify_format\": \"YYYY-MM-DD HH:mm:ss\"\n        },\n        \"sitemap\": {\n            \"hostname\": \"http://pytorch.apachecn.org\"\n        }\n    },\n    \"my_links\" : {\n        \"sidebar\" : {\n            \"Home\" : \"https://www.baidu.com\"\n        }\n    },\n    \"my_plugins\": [\n        \"donate\",\n        \"todo\",\n        \"-lunr\",\n        \"-search\",\n        \"expandable-chapters-small\",\n        \"chapter-fold\",\n        \"expandable-chapters\",\n        \"expandable-chapters-small\",\n        \"back-to-top-button\",\n        \"ga\",\n        \"baidu\",\n        \"sitemap\",\n        \"tbfed-pagefooter\",\n        \"advanced-emoji\",\n        \"sectionx\",\n        \"page-treeview\",\n        \"simple-page-toc\",\n        \"ancre-navigation\",\n        \"theme-apachecn@git+https://github.com/apachecn/theme-apachecn#HEAD\",\n        \"pagefooter-apachecn@git+https://github.com/apachecn/gitbook-plugin-pagefooter-apachecn#HEAD\"\n    ],\n    \"my_pluginsConfig\": {\n        \"github-buttons\": {\n            \"buttons\": [\n              {\n                \"user\": \"apachecn\",\n                \"repo\": \"Interview\", \n                \"type\": \"star\",\n                \"count\": true,\n                \"size\": \"small\"\n              }, \n              {\n                \"user\": \"apachecn\",\n                \"width\": \"160\", \n                \"type\": \"follow\", \n                \"count\": true,\n                \"size\": \"small\"\n              }\n            ]\n        },\n        \"ignores\": [\"node_modules\"],\n        \"simple-page-toc\": {\n            \"maxDepth\": 3,\n            \"skipFirstH1\": true\n        },\n        \"page-toc-button\": {\n            \"maxTocDepth\": 2,\n            \"minTocSize\": 2\n        },\n        \"page-treeview\": {\n            \"copyright\": \"Copyright &#169; aleen42\",\n            \"minHeaderCount\": \"2\",\n            \"minHeaderDeep\": \"2\"\n        },\n        \"donate\": {\n        \t\"wechat\": \"微信收款的二维码URL\",\n        \t\"alipay\": \"支付宝收款的二维码URL\",\n        \t\"title\": \"\",\n        \t\"button\": \"赏\",\n        \t\"alipayText\": \"支付宝打赏\",\n        \t\"wechatText\": \"微信打赏\"\n    \t},\n        \"page-copyright\": {\n            \"description\": \"modified at\",\n            \"signature\": \"你的签名\",\n            \"wisdom\": \"Designer, Frontend Developer & overall web enthusiast\",\n            \"format\": \"YYYY-MM-dd hh:mm:ss\",\n            \"copyright\": \"Copyright &#169; 你的名字\",\n            \"timeColor\": \"#666\",\n            \"copyrightColor\": \"#666\",\n            \"utcOffset\": \"8\",\n            \"style\": \"normal\",\n            \"noPowered\": false\n          },\n          \"ga\": {\n              \"token\": \"UA-127082511-1\"\n          },\n          \"baidu\": {\n              \"token\": \"84fca651656bc67b4b2d56605b6d0852\"\n          },\n        \"pagefooter-apachecn\": {\n            \"copyright\":\"Copyright &copy ibooker.org.cn 2019\",\n            \"modify_label\": \"该文件修订时间： \",\n            \"modify_format\": \"YYYY-MM-DD HH:mm:ss\"\n        }\n    }\n}\n"
  },
  {
    "path": "old/config",
    "content": "[http]  \npostBuffer = 524288000\n"
  },
  {
    "path": "old/requirements.txt",
    "content": "torch\nkeras\nnumpy\npandas\nsklearn\n"
  },
  {
    "path": "old/run_website.sh",
    "content": "#!/bin/bash\nloginfo() { echo \"[INFO] $@\"; }\nlogerror() { echo \"[ERROR] $@\" 1>&2; }\n\npython3 src/script.py \"home\" \"book\"\nrm -rf node_modules/gitbook-plugin-tbfed-pagefooter\ngitbook install\npython3 src/script.py \"home\" \"powered\"\npython3 src/script.py \"home\" \"gitalk\"\ngitbook build ./ _book\n# python3 src/script.py \"home\" \"index\"\n\nversions=\"Algorithm/Leetcode/Python Algorithm/Leetcode/JavaScript\"\nfor version in $versions;do\n    loginfo \"===========================================================\"\n    loginfo \"开始\", ${version}, \"版本编译\"\n\n    echo \"cp book.json docs/${version}\"\n    cp book.json docs/${version}\n\n    # 替换 book.json 的编辑地址\n    echo \"python3 src/script.py ${version} book\"\n    python3 src/script.py ${version} \"book\"\n\n    echo \"cp -r node_modules docs/${version}\"\n    rm -rf docs/${version}/node_modules\n    cp -r node_modules docs/${version}\n\n    echo \"gitbook install docs/${version}\"\n    gitbook install docs/${version}\n\n    echo \"python3 src/script.py ${version} powered\"\n    python3 src/script.py ${version} \"powered\"\n\n    echo \"python3 src/script.py ${version} gitalk\"\n    python3 src/script.py ${version} \"gitalk\"\n\n    echo \"gitbook build docs/${version} _book/docs/${version}\"\n    gitbook build docs/${version} _book/docs/${version}\n\n    # 注释多余的内容\n    # echo \"python3 src/script.py ${version} index\"\n    # python3 src/script.py ${version} \"index\"\ndone\n\n# rm -rf /opt/apache-tomcat-9.0.17/webapps/test_book\n# cp -r _book /opt/apache-tomcat-9.0.17/webapps/test_book\n"
  },
  {
    "path": "src/config_nowcoder.json",
    "content": "{\n    \"name\": \"牛客前端岗面试求职真题解析 2021~2022\",\n    \"url\": \"https://www.nowcoder.com/tutorial/10091/fdde7d829d5c451d9b38a2ff042a6a28\",\n    \"link\": \".sub-menu-tit>h3, .sub-menu-items dd a\",\n    \"title\": \".study-tit>h2\",\n    \"content\": \"article\",\n    \"remove\": \"\",\n    \"credit\": true,\n    \"optiMode\": \"quant\",\n    \"headers\": {\n        \"Cookie\": \"<cookie>\"\n    }\n}"
  },
  {
    "path": "src/do_dir_structure.py",
    "content": "#!/usr/bin/python3\n# coding: utf8\nimport os\nimport sys\n# 自定义包(添加：中间件)\nsys.path.append(os.getcwd())\nfrom Middleware.tool import get_dir_files\n\ncatalog = \"docs/Algorithm/Leetcode/JavaScript\"\nfiles_path = get_dir_files(catalog, [], status=-1, str1=\".DS_Store\")\n# print(files_path)\n\nfor line in files_path:\n    if \"ipynb\" not in line:\n        l_file = line.split(\"/\")[-2:]\n        filename = \"%s %s\" % (l_file[-1].split(\".\")[0], l_file[-1].split(\".\")[1].replace(\"_\", \" \").strip())\n        filepath = \"    * [%s](%s)\" % (filename, \"/\".join(l_file[-1:]))\n        # print(\">>> \", filepath)\n        print(filepath)"
  },
  {
    "path": "src/py2.x/TreeRecursionIterator.py",
    "content": "# coding:utf8\n\nfrom __future__ import print_function\nclass Node():\n    def __init__(self, value, left=None, right=None):\n        self.value = value\n        self.left = left\n        self.right = right\n\ndef midRecusion(node):\n    if node is None:\n        return\n    midRecusion(node.left)\n    print(node.value, end=' ')\n    midRecusion(node.right)\n\ndef midIterator(node):\n    stack = []\n    while stack or node:\n        if node is not None:\n            stack.append(node)\n            node = node.left\n        else:\n            node = stack.pop(-1)\n            print(node.value, end=' ')\n            node = node.right\n\nif __name__ == \"__main__\":\n    node = Node(\"D\", Node(\"B\", Node(\"A\"), Node(\"C\")), Node(\"E\", right=Node(\"G\", left=Node(\"F\"))))\n\n    print('\\n中序遍历<递归>：')\n    midRecusion(node)\n\n    print('\\n中序遍历<迭代>：')\n    midIterator(node)"
  },
  {
    "path": "src/py2.x/list2iteration.py",
    "content": "#!/usr/bin/python\n# coding:utf8\n'''\n迭代使用的是循环结构。\n递归使用的是选择结构。\n'''\nfrom __future__ import print_function\n\n# 递归求解\ndef calculate(l):\n    if len(l) <= 1:\n        return l[0]\n    value = calculate(l[1:])\n    return 10**(len(l) - 1) * l[0] + value\n\n\n# 迭代求解\ndef calculate2(l):\n    result = 0\n    while len(l) >= 1:\n        result += 10 ** (len(l)-1) * l[0]\n        l = l[1:]\n    return result\n\n\nl1 = [1, 2, 3]\nl2 = [4, 5]\nsum = 0\nresult = calculate(l1) + calculate(l2)\n# result = calculate2(l1) + calculate2(l2)\nprint(result)\n"
  },
  {
    "path": "src/py3.x/DataStructure/BinarySearch.py",
    "content": "\"\"\"\n1. 二分查找是有条件的，首先是有序，其次因为二分查找操作的是下标，所以要求是顺序表\n2. 最优时间复杂度：O(1)\n3. 最坏时间复杂度：O(logn)\n\"\"\"\n# def binary_search(nums, data):\n#     \"\"\"\n#     非递归解决二分查找\n#     :param nums:\n#     :return:\n#     \"\"\"\n#     n = len(nums)\n#     first = 0\n#     last = n - 1\n#     while first <= last:\n#         mid = (last + first) // 2\n#         if nums[mid] > data:\n#             last = mid - 1\n#         elif nums[mid] < data:\n#             first = mid + 1\n#         else:\n#             return True\n#     return False\n\n\ndef binary_search(nums, data):\n    \"\"\"\n    递归解决二分查找: nums 是一个有序数组\n    :param nums:\n    :return:\n    \"\"\"\n    n = len(nums)\n    if n < 1:\n        return False\n    mid = n // 2\n    if nums[mid] > data:\n        return binary_search(nums[:mid], data)\n    elif nums[mid] < data:\n        return binary_search(nums[mid+1:], data)\n    else:\n        return True\n\nif __name__ == '__main__':\n    nums = [1, 4, 6, 8, 10, 20, 25, 30]\n    if binary_search(nums, 8):\n        print('ok')\n"
  },
  {
    "path": "src/py3.x/DataStructure/BubbleSort.py",
    "content": "# coding:utf-8\n\"\"\"\n# 冒泡排序\n# 1. 外层循环负责循环的次数，依次递减到1就停止(1个数不存在下一个值)\n# 2. 内层循环负责前后两两比较, 判断是否需要交换位置，然后移动判断\n\n5个数\n5  0，1，2，3，4\n4  0，1，2，3\n3  0，1，2\n2  0，1\n\"\"\"\n\ndef bubble_sort(nums):\n    # 判断外出循环的次数\n    index = len(nums) - 1\n    while index:\n        print(index)\n        # 第一个数字，和后面每一个数字进行对比，找出最大值，放到最后！！\n        for i in range(index):\n            if nums[i] > nums[i+1]:\n                    nums[i], nums[i+1] = nums[i+1], nums[i]\n        index -= 1\n\n\nif __name__ == \"__main__\":\n    nums = [3, 6, 8, 5, 2, 4, 9, 1, 7]\n    bubble_sort(nums)\n    print('result:', nums)\n"
  },
  {
    "path": "src/py3.x/DataStructure/InsertionSort.py",
    "content": "# coding:utf8\n\"\"\"\n插入排序和冒泡排序的区别在于：\n\n插入排序的前提是：左边是有序的数列\n而冒泡排序：相邻的值进行交换，一共进行n次交换\n\"\"\"\ndef insertion_sort(nums):\n    for i in range(1, len(nums)):\n        while i:\n            if nums[i] < nums[i-1]:\n                nums[i], nums[i-1] = nums[i-1], nums[i]\n            i -= 1\n    return nums\n\n\nif __name__ == \"__main__\":\n    nums = [3, 6, 8, 5, 2, 4, 9, 1, 7]\n    insertion_sort(nums)\n    print('result:', nums)\n"
  },
  {
    "path": "src/py3.x/DataStructure/MergeSort.py",
    "content": "# coding: utf-8\n\n\ndef MergeSort(nums):\n    if len(nums) <= 1:\n        return nums\n    num = int(len(nums)/2)\n    # 从中间，进行数据的拆分, 递归的返回数据进行迭代排序\n    left = MergeSort(nums[:num])\n    right = MergeSort(nums[num:])\n    print(\"left: \", left)\n    print(\"right: \", right)\n    print(\"-\" * 20)\n    return Merge(left, right)\n\n\ndef Merge(left, right):\n    l, r = 0, 0\n    result = []\n    while l < len(left) and r < len(right):\n        if left[l] < right[r]:\n            result.append(left[l])\n            l += 1\n        else:\n            result.append(right[r])\n            r += 1\n    result += left[l:]\n    result += right[r:]\n    return result\n\n\nif __name__ == \"__main__\":\n    nums = [2, 6, 8, 5, 1, 4, 9, 3, 7]\n    nums = MergeSort(nums)\n    print('result:', nums)\n"
  },
  {
    "path": "src/py3.x/DataStructure/QuickSort.py",
    "content": "#!/usr/bin/python\n# coding:utf8\ndef quick_sort(nums, start, end):\n    i = start\n    j = end\n    # 结束排序\n    if i >= j:\n        return\n    # 保存首个数值\n    key = nums[i]\n    # 一次排序，i和j的值不断的靠拢，然后最终停止，结束一次排序\n    while i < j:\n        # 和最右边的比较，如果>=key,然后j-1，慢慢的和前一个值比较;如果值<key，那么就交换位置\n        while i < j and key <= nums[j]:\n            print(key, nums[j], '*' * 30)\n            j -= 1\n        nums[i] = nums[j]\n        # 交换位置后，然后在和最左边的值开始比较，如果<=key,然后i+1，慢慢的和后一个值比较;如果值>key，那么就交换位置\n        while i < j and key >= nums[i]:\n            print(key, nums[i], '*' * 30)\n            i += 1\n        nums[j] = nums[i]\n    nums[i] = key\n    # 左边排序\n    quick_sort(nums, start, i-1)\n    # 右边排序\n    quick_sort(nums, i+1, end)\n\n\nif __name__ == \"__main__\":\n    nums = [3, 6, 8, 5, 2, 4, 9, 1, 7]\n    quick_sort(nums, 0, len(nums) - 1)\n    print('result:', nums)\n"
  },
  {
    "path": "src/py3.x/DataStructure/RadixSort.py",
    "content": "#************************基数排序****************************\n#确定排序的次数\n#排序的顺序跟序列中最大数的位数相关\ndef radix_sort_nums(L):\n    maxNum = L[0]\n#寻找序列中的最大数\n    for x in L:\n        if maxNum < x:\n            maxNum = x\n#确定序列中的最大元素的位数\n    times = 0\n    while (maxNum > 0):\n        maxNum = int((maxNum/10))\n        times += 1\n    return times\n#找到num从低到高第pos位的数据\ndef get_num_pos(num, pos):\n    return (int((num/(10**(pos-1))))) % 10\n#基数排序\ndef radix_sort(L):\n    count = 10 * [None]       #存放各个桶的数据统计个数\n    bucket = len(L) * [None]  #暂时存放排序结果\n#从低位到高位依次执行循环\n    for pos in range(1, radix_sort_nums(L)+1):\n        #置空各个桶的数据统计\n        for x in range(0, 10):\n            count[x] = 0\n        #统计当前该位(个位，十位，百位....)的元素数目\n        for x in range(0, len(L)):\n            #统计各个桶将要装进去的元素个数\n            j = get_num_pos(int(L[x]), pos)\n            count[j] += 1\n        #count[i]表示第i个桶的右边界索引\n        for x in range(1,10):\n            count[x] += count[x-1]\n        #将数据依次装入桶中\n        for x in range(len(L)-1, -1, -1):\n            #求出元素第K位的数字\n            j = get_num_pos(L[x], pos)\n            #放入对应的桶中，count[j]-1是第j个桶的右边界索引\n            bucket[count[j]-1] = L[x]\n            #对应桶的装入数据索引-1\n            count[j] -= 1\n        # 将已分配好的桶中数据再倒出来，此时已是对应当前位数有序的表\n        for x in range(0, len(L)):\n            L[x] = bucket[x]\n"
  },
  {
    "path": "src/py3.x/DataStructure/SelectionSort.py",
    "content": "# coding:utf8\n\"\"\"\n选择排序和冒泡排序的区别在于：\n\n选择排序的前提是：找到最小值的位置，最后才进行1次交换\n而冒泡排序：相邻的值进行交换，一共进行n次交换\n\"\"\"\ndef selection_sort(nums):\n    for i in range(len(nums)-1):\n        index = i\n        # 考虑到数组会遇到多个最小值，所以比较的时候直接用index表示当前比较最小\n        for j in range(i+1, len(nums)):\n            if nums[index] > nums[j]:\n                index = j\n        nums[i], nums[index] = nums[index], nums[i]\n\n\nif __name__ == \"__main__\":\n    nums = [3, 6, 8, 5, 2, 4, 9, 1, 7]\n    selection_sort(nums)\n    print('result:', nums)\n"
  },
  {
    "path": "src/py3.x/DataStructure/ShellSort.py",
    "content": "# coding: utf8\n\nfrom __future__ import print_function\ndef insert_sort(l, start, increment):\n    for i in range(start+increment, len(l), increment):\n        for j in range(start, len(l[:i]), increment):\n            if l[i] < l[j]:\n                l[i], l[j] = l[j], l[i]\n    print(increment, '--',l)\n    return l\n\ndef shell_sort(l, increment):\n    # 依次进行分层\n    while increment:\n        # 每一层，都进行n次插入排序\n        for i in range(0, increment):\n            insert_sort(l, i, increment)\n        increment -= 1\n    return l\n\nif __name__ == \"__main__\":\n    l = [5, 2, 9, 8, 1, 10, 3, 4, 7]\n    increment = len(l)/3+1 if len(l)%3 else len(l)/3\n    print(\"开始\", l)\n    l = shell_sort(l, increment)\n    print(\"结束\", l)"
  },
  {
    "path": "src/py3.x/TreeRecursionIterator.py",
    "content": "# coding:utf8\n\nfrom __future__ import print_function\nclass Node():\n    def __init__(self, value, left=None, right=None):\n        self.value = value\n        self.left = left\n        self.right = right\n\ndef midRecusion(node):\n    if node is None:\n        return\n    midRecusion(node.left)\n    print(node.value, end=' ')\n    midRecusion(node.right)\n\ndef midIterator(node):\n    stack = []\n    while stack or node:\n        if node is not None:\n            stack.append(node)\n            node = node.left\n        else:\n            node = stack.pop(-1)\n            print(node.value, end=' ')\n            node = node.right\n\nif __name__ == \"__main__\":\n    node = Node(\"D\", Node(\"B\", Node(\"A\"), Node(\"C\")), Node(\"E\", right=Node(\"G\", left=Node(\"F\"))))\n\n    print('\\n中序遍历<递归>：')\n    midRecusion(node)\n\n    print('\\n中序遍历<迭代>：')\n    midIterator(node)"
  },
  {
    "path": "src/py3.x/kaggle/featured/mercari-price-suggestion-challenge/script.py",
    "content": "\nimport pyximport; pyximport.install()\nimport gc\nimport time\nimport numpy as np\nimport pandas as pd\nfrom sklearn.decomposition import TruncatedSVD\n#svd = TruncatedSVD(n_components=1000, random_state=42)\n\nfrom joblib import Parallel, delayed\n\nfrom scipy.sparse import csr_matrix, hstack\n\nfrom sklearn.linear_model import Ridge\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.model_selection import train_test_split, cross_val_score\nfrom sklearn.linear_model import SGDRegressor\nimport lightgbm as lgb\n\nNUM_BRANDS = 4500\nNUM_CATEGORIES = 1200\nNAME_MIN_DF = 10\nMAX_FEATURES_ITEM_DESCRIPTION = 180000\n\n\ndef rmsle(y, y0):\n     assert len(y) == len(y0)\n     return np.sqrt(np.mean(np.power(np.log1p(y)-np.log1p(y0), 2)))\n    \ndef split_cat(text):\n    try: return text.split(\"/\")\n    except: return (\"No Label\", \"No Label\", \"No Label\")\n    \ndef handle_missing_inplace(dataset):\n    dataset['general_cat'].fillna(value='missing', inplace=True)\n    dataset['subcat_1'].fillna(value='missing', inplace=True)\n    dataset['subcat_2'].fillna(value='missing', inplace=True)\n    dataset['brand_name'].fillna(value='missing', inplace=True)\n    dataset['item_description'].fillna(value='missing', inplace=True)\n\n\ndef cutting(dataset):\n    pop_brand = dataset['brand_name'].value_counts().loc[lambda x: x.index != 'missing'].index[:NUM_BRANDS]\n    dataset.loc[~dataset['brand_name'].isin(pop_brand), 'brand_name'] = 'missing'\n    pop_category1 = dataset['general_cat'].value_counts().loc[lambda x: x.index != 'missing'].index[:NUM_CATEGORIES]\n    pop_category2 = dataset['subcat_1'].value_counts().loc[lambda x: x.index != 'missing'].index[:NUM_CATEGORIES]\n    pop_category3 = dataset['subcat_2'].value_counts().loc[lambda x: x.index != 'missing'].index[:NUM_CATEGORIES]\n    dataset.loc[~dataset['general_cat'].isin(pop_category1), 'general_cat'] = 'missing'\n    dataset.loc[~dataset['subcat_1'].isin(pop_category2), 'subcat_1'] = 'missing'\n    dataset.loc[~dataset['subcat_2'].isin(pop_category3), 'subcat_2'] = 'missing'\n\n\ndef to_categorical(dataset):\n    dataset['general_cat'] = dataset['general_cat'].astype('category')\n    dataset['subcat_1'] = dataset['subcat_1'].astype('category')\n    dataset['subcat_2'] = dataset['subcat_2'].astype('category')\n    dataset['item_condition_id'] = dataset['item_condition_id'].astype('category')\n\n\ndef main():\n    start_time = time.time()\n\n    train = pd.read_table('../input/train.tsv', engine='c')\n    test = pd.read_table('../input/test.tsv', engine='c')\n    print('[{}] Finished to load data'.format(time.time() - start_time))\n    print('Train shape: ', train.shape)\n    print('Test shape: ', test.shape)\n    nrow_test = train.shape[0] #-dftt.shape[0]\n    dftt = train[(train.price < 1.0)]\n    train = train.drop(train[(train.price < 1.0)].index)\n    del dftt['price']\n    nrow_train = train.shape[0] #-dftt.shape[0]\n    #nrow_test = train.shape[0] + dftt.shape[0]\n    y = np.log1p(train[\"price\"])\n    merge: pd.DataFrame = pd.concat([train, dftt, test])\n    submission: pd.DataFrame = test[['test_id']]\n    submission2: pd.DataFrame = test[['test_id']]\n\n    del train\n    del test\n    gc.collect()\n    \n    merge['general_cat'], merge['subcat_1'], merge['subcat_2'] = \\\n    zip(*merge['category_name'].apply(lambda x: split_cat(x)))\n    merge.drop('category_name', axis=1, inplace=True)\n    print('[{}] Split categories completed.'.format(time.time() - start_time))\n\n    handle_missing_inplace(merge)\n    print('[{}] Handle missing completed.'.format(time.time() - start_time))\n\n    cutting(merge)\n    print('[{}] Cut completed.'.format(time.time() - start_time))\n\n    to_categorical(merge)\n    print('[{}] Convert categorical completed'.format(time.time() - start_time))\n\n    cv = CountVectorizer(min_df=NAME_MIN_DF,ngram_range=(1, 2),\n        token_pattern=r'\\b\\w+\\b|\\w?-\\w+', stop_words = 'english')\n    X_name = cv.fit_transform(merge['name'])\n    print('[{}] Count vectorize `name` completed.'.format(time.time() - start_time))\n\n    cv = CountVectorizer()\n    X_category1 = cv.fit_transform(merge['general_cat'])\n    X_category2 = cv.fit_transform(merge['subcat_1'])\n    X_category3 = cv.fit_transform(merge['subcat_2'])\n    print('[{}] Count vectorize `categories` completed.'.format(time.time() - start_time))\n\n    tv = TfidfVectorizer(max_features=MAX_FEATURES_ITEM_DESCRIPTION,\n                         ngram_range=(1, 2),\n                         token_pattern=r'\\w+|\\d\\w+',)\n    X_description = tv.fit_transform(merge['item_description'])\n    print('[{}] TFIDF vectorize `item_description` completed.'.format(time.time() - start_time))\n\n    lb = LabelBinarizer(sparse_output=True)\n    X_brand = lb.fit_transform(merge['brand_name'])\n    print('[{}] Label binarize `brand_name` completed.'.format(time.time() - start_time))\n\n    X_dummies = csr_matrix(pd.get_dummies(merge[['item_condition_id', 'shipping']],\n                                          sparse=True).values)\n    print('[{}] Get dummies on `item_condition_id` and `shipping` completed.'.format(time.time() - start_time))\n    print (X_dummies.shape, X_description.shape, X_brand.shape, X_category1.shape, X_category2.shape, X_category3.shape, X_name.shape)\n    sparse_merge = hstack((X_dummies, X_description, X_brand, X_category1, X_category2, X_category3, X_name)).tocsr()\n    print('[{}] Create sparse merge completed'.format(time.time() - start_time))\n\n    X = sparse_merge[:nrow_train]\n    X_test = sparse_merge[nrow_test:]\n    \n    model = Ridge(alpha=.6, copy_X=True, fit_intercept=True, max_iter=100,\n      normalize=False, random_state=101, solver='auto', tol=0.05)\n    model.fit(X, y)\n    print('[{}] Train ridge completed'.format(time.time() - start_time))\n    predsR = model.predict(X=X_test)\n    print('[{}] Predict ridge completed'.format(time.time() - start_time))\n    \n    model = Ridge(solver='sag', fit_intercept=True, tol=0.05)\n    model.fit(X, y)\n    print('[{}] Train ridge v2 completed'.format(time.time() - start_time))\n    predsR2 = model.predict(X=X_test)\n    print('[{}] Predict ridge v2 completed'.format(time.time() - start_time))\n\n    model = Ridge(alpha=.6, copy_X=True, fit_intercept=True, max_iter=100,\n    normalize=False, random_state=101, solver='auto', tol=0.05)\n    model.fit(X, y)\n    print('[{}] Train ridge completed'.format(time.time() - start_time))\n    predsR3 = model.predict(X=X_test)\n    print('[{}] Predict ridge completed'.format(time.time() - start_time))\n    \n    model = Ridge(solver='sag', fit_intercept=True, tol=0.05)\n    model.fit(X, y)\n    print('[{}] Train ridge v2 completed'.format(time.time() - start_time))\n    predsR4 = model.predict(X=X_test)\n    print('[{}] Predict ridge v2 completed'.format(time.time() - start_time))\n    \n    \n    train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size = 0.16, random_state = 144) \n    d_train = lgb.Dataset(train_X, label=train_y)\n    d_valid = lgb.Dataset(valid_X, label=valid_y)\n    watchlist = [d_train, d_valid]\n    \n    params = {\n         'max_bin': 8192,\n        'learning_rate': 0.60,\n        'application': 'regression',\n        'max_depth': 3,\n        'num_leaves': 60,\n        'verbosity': -1,\n        'metric': 'RMSE',\n        'data_random_seed': 2,\n        'bagging_fraction': 0.5,\n        'nthread': 4,\n        'tree_learner' : 'data',\n    }\n\n    model = lgb.train(params, train_set=d_train, num_boost_round=8500, valid_sets=watchlist, \\\n    early_stopping_rounds=1000, verbose_eval=1000) \n    predsL = model.predict(X_test)\n    \n    print('[{}] Predict lgb 1 completed.'.format(time.time() - start_time))\n\n\n    preds = (predsR*0.15 + predsL*0.4  + predsR2*0.15 + predsR3*0.15 + predsR4*0.15)\n\n    submission['price'] = np.expm1(preds)\n    submission.to_csv(\"submission_ridge_2xlgbm.csv\", index=False)\n    \n    preds2 = (predsR*0.1 + predsL*0.6  + predsR2*0.1 + predsR3*0.1 + predsR4*0.1)\n    submission2['price'] = np.expm1(preds2)\n    submission.to_csv(\"submission_ridge_2xlgbm2.csv\", index=False)\n\n\nif __name__ == '__main__':\n    main()"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/digit-recognizer/cnn_keras-python3.6.py",
    "content": "#!/usr/bin/python\n# coding: utf-8\n'''\nCreated on 2017-12-26\nUpdate  on 2017-12-26\nAuthor: xiaomingnio\nGithub: https://github.com/apachecn/kaggle\n'''\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\nfrom keras.callbacks import ReduceLROnPlateau\nfrom keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPool2D\nfrom keras.models import Sequential\nfrom keras.optimizers import RMSprop\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.utils.np_utils import to_categorical  # convert to one-hot-encoding\nimport os\n\nnp.random.seed(2)\n\n# 数据路径\ndata_dir = '/media/wsw/B634091A3408DF6D/data/kaggle/datasets/getting-started/digit-recognizer/'\n\n# Load the data\ntrain = pd.read_csv(os.path.join(data_dir, 'input/train.csv'))\ntest = pd.read_csv(os.path.join(data_dir, 'input/test.csv'))\n\nX_train = train.values[:, 1:]\nY_train = train.values[:, 0]\ntest = test.values\n\n# Normalization\nX_train = X_train / 255.0\ntest = test / 255.0\n\n# Reshape image in 3 dimensions (height = 28px, width = 28px , canal = 1)\nX_train = X_train.reshape(-1, 28, 28, 1)\ntest = test.reshape(-1, 28, 28, 1)\n\n# Encode labels to one hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,0,0])\nY_train = to_categorical(Y_train, num_classes=10)\n\n# Set the random seed\nrandom_seed = 2\n\n# Split the train and the validation set for the fitting\nX_train, X_val, Y_train, Y_val = train_test_split(\n    X_train, Y_train, test_size=0.1, random_state=random_seed)\n\n# Set the CNN model \n# my CNN architechture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out\n\nmodel = Sequential()\n\nmodel.add(\n    Conv2D(\n        filters=32,\n        kernel_size=(5, 5),\n        padding='Same',\n        activation='relu',\n        input_shape=(28, 28, 1)))\nmodel.add(\n    Conv2D(\n        filters=32, kernel_size=(5, 5), padding='Same', activation='relu'))\nmodel.add(MaxPool2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(\n    Conv2D(\n        filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))\nmodel.add(\n    Conv2D(\n        filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))\nmodel.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Flatten())\nmodel.add(Dense(256, activation=\"relu\"))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(10, activation=\"softmax\"))\n\n# Define the optimizer\noptimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\n\n# Compile the model\nmodel.compile(\n    optimizer=optimizer, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n\nepochs = 30\nbatch_size = 86\n\n# Set a learning rate annealer\nlearning_rate_reduction = ReduceLROnPlateau(\n    monitor='val_acc', patience=3, verbose=1, factor=0.5, min_lr=0.00001)\n\ndatagen = ImageDataGenerator(\n    featurewise_center=False,  # set input mean to 0 over the dataset\n    samplewise_center=False,  # set each sample mean to 0\n    featurewise_std_normalization=False,  # divide inputs by std of the dataset\n    samplewise_std_normalization=False,  # divide each input by its std\n    zca_whitening=False,  # apply ZCA whitening\n    rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)\n    zoom_range=0.1,  # Randomly zoom image \n    width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n    height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n    horizontal_flip=False,  # randomly flip images\n    vertical_flip=False)  # randomly flip images\n\ndatagen.fit(X_train)\n\nhistory = model.fit_generator(\n    datagen.flow(\n        X_train, Y_train, batch_size=batch_size),\n    epochs=epochs,\n    validation_data=(X_val, Y_val),\n    verbose=2,\n    steps_per_epoch=X_train.shape[0] // batch_size,\n    callbacks=[learning_rate_reduction])\n\n# predict results\nresults = model.predict(test)\n\n# select the indix with the maximum probability\nresults = np.argmax(results, axis=1)\n\nresults = pd.Series(results, name=\"Label\")\n\nsubmission = pd.concat(\n    [pd.Series(\n        range(1, 28001), name=\"ImageId\"), results], axis=1)\n\nsubmission.to_csv(os.path.join(data_dir, \"output/Result_keras_CNN.csv\",index=False))\nprint('finished')\n"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/digit-recognizer/cnn_pytorch-python3.6.py",
    "content": "#!/usr/bin/python3\n# coding: utf-8\n'''\nCreated on 2017-12-18\nUpdate  on 2018-03-27\nAuthor: 片刻\nGithub: https://github.com/apachecn/kaggle\nResult: \n    BATCH_SIZE = 10 and EPOCH = 10; [10,  4000] loss: 0.069\n    BATCH_SIZE = 10 and EPOCH = 15; [10,  4000] loss: 0.069\n'''\n# import csv\nimport pandas as pd\n\n# third-party library\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.utils.data import Dataset, DataLoader\nimport os.path\n\n# 数据路径\ndata_dir = '/opt/data/kaggle/getting-started/digit-recognizer/'\n\nclass CustomedDataSet(Dataset):\n    def __init__(self, train=True):\n        self.train = train\n        if self.train:\n            trainX = pd.read_csv(\n                os.path.join(data_dir, 'input/train.csv')\n                # names=[\"ImageId\", \"Label\"]\n            )\n            trainY = trainX.label.as_matrix().tolist()\n            trainX = trainX.drop(\n                'label', axis=1).as_matrix().reshape(trainX.shape[0], 1, 28, 28)\n            self.datalist = trainX\n            self.labellist = trainY\n        else:\n            testX = pd.read_csv(\n                os.path.join(data_dir, 'input/test.csv')\n            )\n            self.testID = testX.index\n            testX = testX.as_matrix().reshape(testX.shape[0], 1, 28, 28)\n            self.datalist = testX\n\n    def __getitem__(self, index):\n        if self.train:\n            return torch.Tensor(\n                self.datalist[index].astype(float)), self.labellist[index]\n        else:\n            return torch.Tensor(self.datalist[index].astype(float))\n\n    def __len__(self):\n        return self.datalist.shape[0]\n\n\ntrain_data = CustomedDataSet()\ntest_data = CustomedDataSet(train=False)\n\nBATCH_SIZE = 150\ntrain_loader = DataLoader(\n    dataset=train_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)\ntest_loader = DataLoader(\n    dataset=test_data, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)\n\n\nclass CNN(nn.Module):\n    def __init__(self):\n        super(CNN, self).__init__()\n        self.conv1 = nn.Sequential(  # input shape (1, 28, 28)\n            nn.Conv2d(\n                in_channels=1,  # input height\n                out_channels=16,  # n_filters\n                kernel_size=5,  # filter size\n                stride=1,  # filter movement/step\n                padding=2,  # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1\n            ),  # output shape (16, 28, 28)\n            nn.ReLU(),  # activation\n            nn.MaxPool2d(\n                kernel_size=2\n            ),  # choose max value in 2x2 area, output shape (16, 14, 14)\n        )\n        self.conv2 = nn.Sequential(  # input shape (1, 14, 14)\n            nn.Conv2d(16, 32, 5, 1, 2),  # output shape (32, 14, 14)\n            nn.ReLU(),  # activation\n            nn.MaxPool2d(2),  # output shape (32, 7, 7)\n        )\n        self.out = nn.Linear(32 * 7 * 7,\n                             10)  # fully connected layer, output 10 classes\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.conv2(x)\n        x = x.view(\n            x.size(0),\n            -1)  # flatten the output of conv2 to (batch_size, 32 * 7 * 7)\n        output = self.out(x)\n        return output, x  # return x for visualization\n\n\ncnn = CNN()\n# print(cnn)  # net architecture\n\nLR = 0.001  # learning rate\noptimizer = torch.optim.Adam(\n    cnn.parameters(), lr=LR)  # optimize all cnn parameters\nloss_func = nn.CrossEntropyLoss()  # the target label is not one-hotted\n\n# training and testing\nprint(u'开始训练')\nEPOCH = 5  # train the training data n times, to save time, we just train 1 epoch\nfor epoch in range(EPOCH):\n    running_loss = 0.0\n\n    for step, (x, y) in enumerate(\n            train_loader\n    ):  # gives batch data, normalize x when iterate train_loader\n        b_x = Variable(x)  # batch x\n        b_y = Variable(y)  # batch y\n\n        output = cnn(b_x)[0]  # 输入训练数据\n        loss = loss_func(output, b_y)  # 计算误差\n        optimizer.zero_grad()  # 清空上一次梯度\n        loss.backward()  # 误差反向传递\n        optimizer.step()  # 优化器参数更新\n\n        # 每1000批数据打印一次平均loss值\n        running_loss += loss.data[\n            0]  # loss本身为Variable类型，所以要使用data获取其Tensor，因为其为标量，所以取0\n        if step % 500 == 499:  # 每2000批打印一次\n            print('[%d, %5d] loss: %.3f' %\n                  (epoch + 1, step + 1, running_loss / 500))\n            running_loss = 0.0\nprint('Finished Training')\n\n# correct = 0\n# total = 0\n# for img, label in test_loader:\n#     img = Variable(img, volatile=True)\n#     label = Variable(label, volatile=True)\n\n#     outputs = cnn(img)\n#     _, predicted = torch.max(outputs[0], 1)\n#     # print('1-', type(label), '-------', label)\n#     # print('2-', type(predicted), '-------', predicted)\n#     total += label.size(0)\n#     num_correct = (predicted == label).sum()\n#     correct += num_correct.data[0]\n\n# print('Accuracy of the network on the %d test images: %.3f %%' % (total, 100 * correct / total))\n\n# I just can't throw all of test data into the network,since it was so huge that my GPU memory cann't afford it\nans = torch.LongTensor()  # build a tensor to concatenate answers\nfor img in test_loader:\n    img = Variable(img)\n    outputs = cnn(img)\n    _, predicted = torch.max(outputs[0], 1)\n    # print('type(predicted) = ', type(predicted), predicted)\n    ans = torch.cat([ans, predicted.data], 0)\n\ntestLabel = ans.numpy()  # only tensor on cpu can transform to the numpy array\n\n# # 结果输出保存\n# def saveResult(result, csvName):\n#     with open(csvName, 'w') as myFile:\n#         myWriter = csv.writer(myFile)\n#         myWriter.writerow([\"ImageId\", \"Label\"])\n#         index = 0\n#         for r in result:\n#             index += 1\n#             myWriter.writerow([index, int(r)])\n\n#     print('Saved successfully...')  # 保存预测结果\n\n# saveResult(testLabel,\n#            '/opt/data/kaggle/getting-started/digit-recognizer/output/Result_pytorch_CNN.csv')\n\n# 提交结果\nsubmission_df = pd.DataFrame(\n    data={'ImageId': test_data.testID+1,\n          'Label': testLabel})\n# print(submission_df.head(10))\nsubmission_df.to_csv(\n    os.path.join(data_dir, 'output/Result_pytorch_CNN.csv'),\n    columns=[\"ImageId\", \"Label\"],\n    index=False)\n"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/digit-recognizer/knn-python3.6.py",
    "content": "#!/usr/bin/python\n# coding: utf-8\n'''\nCreated on 2017-10-26\nUpdate  on 2018-05-16\nAuthor: 片刻/ccyf00\nGithub: https://github.com/apachecn/kaggle\n'''\nimport os\nimport csv\nimport datetime\nimport numpy as np\nimport pandas as pd\nfrom sklearn.decomposition import PCA\nfrom sklearn.neighbors import KNeighborsClassifier\n\n\ndata_dir = '/opt/data/kaggle/getting-started/digit-recognizer/'\n\n\n# 加载数据\ndef opencsv():\n    # 使用 pandas 打开\n    data = pd.read_csv(os.path.join(data_dir, 'input/train.csv'))\n    data1 = pd.read_csv(os.path.join(data_dir, 'input/test.csv'))\n\n    train_data = data.values[:, 1:]  # 读入全部训练数据,  [行，列]\n    train_label = data.values[:, 0]  # 读取列表的第一列\n    test_data = data1.values[:, :]  # 测试全部测试个数据\n    return train_data, train_label, test_data\n\n\n# 数据预处理-降维 PCA主成成分分析\ndef dRPCA(x_train, x_test, COMPONENT_NUM):\n    print('dimensionality reduction...')\n    trainData = np.array(x_train)\n    testData = np.array(x_test)\n    '''\n    使用说明：https://www.cnblogs.com/pinard/p/6243025.html\n    n_components>=1\n      n_components=NUM  \b 设置\b占特征数量比\n    0 < n_components < 1\n      n_components=0.99  \b设置阈值总方差占比\n    '''\n    pca = PCA(n_components=COMPONENT_NUM, whiten=False)\n    pca.fit(trainData)  # Fit the model with X\n    pcaTrainData = pca.transform(trainData)  # Fit the model with X and 在X上完成降维.\n    pcaTestData = pca.transform(testData)  # Fit the model with X and 在X上完成降维.\n\n    # pca 方差大小、方差占比、特征数量\n    # print(\"方差大小:\\n\", pca.explained_variance_, \"方差占比:\\n\", pca.explained_variance_ratio_)\n    print(\"特征数量: %s\" % pca.n_components_)\n    print(\"总方差占比: %s\" % sum(pca.explained_variance_ratio_))\n    return pcaTrainData, pcaTestData\n\n\ndef trainModel(trainData, trainLabel):\n    clf = KNeighborsClassifier()  # default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10)\n    clf.fit(trainData, np.ravel(trainLabel))  # ravel Return a contiguous flattened array.\n    return clf\n\n\ndef saveResult(result, csvName):\n    with open(csvName, 'w') as myFile:  # 创建记录输出结果的文件（w 和 wb 使用的时候有问题）\n        # python3里面对 str和bytes类型做了严格的区分，不像python2里面某些函数里可以混用。所以用python3来写wirterow时，打开文件不要用wb模式，只需要使用w模式，然后带上newline=''\n        myWriter = csv.writer(myFile)\n        myWriter.writerow([\"ImageId\", \"Label\"])\n        index = 0\n        for r in result:\n            index += 1\n            myWriter.writerow([index, int(r)])\n    print('Saved successfully...')  # 保存预测结果\n\n\ndef dRecognition_knn():\n    # 开始时间\n    sta_time = datetime.datetime.now()\n\n    # 加载数据\n    trainData, trainLabel, testData = opencsv()\n    # print(\"trainData==>\", type(trainData), shape(trainData))\n    # print(\"trainLabel==>\", type(trainLabel), shape(trainLabel))\n    # print(\"testData==>\", type(testData), shape(testData))\n    print(\"load data finish\")\n    end_time_1 = datetime.datetime.now()\n    print('load data time used: %s' % end_time_1)\n\n    # 降维处理\n    trainDataPCA, testDataPCA = dRPCA(trainData, testData, 0.8)\n\n    # 模型训练\n    clf = trainModel(trainDataPCA, trainLabel)\n    # 结果预测\n    testLabel = clf.predict(testDataPCA)\n\n    # 结果的输出\n    saveResult(testLabel, os.path.join(data_dir, 'output/Result_knn.csv'))\n    print(\"finish!\")\n\n    # 结束时间\n    end_time = datetime.datetime.now()\n    times = (end_time - sta_time).seconds\n    print(\"\\n运行时间: %ss == %sm == %sh\\n\\n\" % (times, times/60, times/60/60))\n\n\nif __name__ == '__main__':\n    dRecognition_knn()\n"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/digit-recognizer/nn-python3.6.py",
    "content": "#!/usr/bin/python\n# coding: utf-8\n'''\nCreated on 2018-05-14\nUpdate  on 2018-05-14\nAuthor: 平淡的天\nGithub: https://github.com/apachecn/kaggle\n'''\n\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.decomposition import PCA\nimport pandas as pd\n\ntrain_data = pd.read_csv(r\"C:\\Users\\312\\Desktop\\digit-recognizer\\train.csv\")\ntest_data = pd.read_csv(r\"C:\\Users\\312\\Desktop\\digit-recognizer\\test.csv\")\ndata = pd.concat([train_data, test_data], axis=0).reset_index(drop=True)\ndata.drop(['label'], axis=1, inplace=True)\nlabel = train_data.label\n\npca = PCA(n_components=100, random_state=34)\ndata_pca = pca.fit_transform(data)\n\nXtrain, Ytrain, xtest, ytest = train_test_split(\n    data_pca[0:len(train_data)], label, test_size=0.1, random_state=34)\n\nclf = MLPClassifier(\n    hidden_layer_sizes=(100, ),\n    activation='relu',\n    alpha=0.0001,\n    learning_rate='constant',\n    learning_rate_init=0.001,\n    max_iter=200,\n    shuffle=True,\n    random_state=34)\n\nclf.fit(Xtrain, xtest)\ny_predict = clf.predict(Ytrain)\n\nzeroLable = ytest - y_predict\nrightCount = 0\nfor i in range(len(zeroLable)):\n    if list(zeroLable)[i] == 0:\n        rightCount += 1\nprint('the right rate is:', float(rightCount) / len(zeroLable))\n\nresult = clf.predict(data_pca[len(train_data):])\n\ni = 0\nfw = open(\"C:\\\\Users\\\\312\\\\Desktop\\\\digit-recognizer\\\\result.csv\", 'w')\nwith open('C:\\\\Users\\\\312\\\\Desktop\\\\digit-recognizer\\\\sample_submission.csv'\n          ) as pred_file:\n    fw.write('{},{}\\n'.format('ImageId', 'Label'))\n    for line in pred_file.readlines()[1:]:\n        splits = line.strip().split(',')\n        fw.write('{},{}\\n'.format(splits[0], result[i]))\n        i += 1\n"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/digit-recognizer/rf-python3.6.py",
    "content": "#!/usr/bin/python\n# coding: utf-8\n'''\nCreated on 2018-05-14\nUpdate  on 2018-05-19\nAuthor: 平淡的天/wang-sw\nGithub: https://github.com/apachecn/kaggle\n'''\nimport os\nimport time\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.decomposition import PCA\nimport pandas as pd\nimport numpy as np\n# from numpy import arange\n# from lightgbm import LGBMClassifier\n# from sklearn.model_selection import GridSearchCV\n\n\n# 数据路径\ndata_dir = '/Users/wuyanxue/Documents/GitHub/datasets/getting-started/digit-recognizer/'\n\n\n# 加载数据\ndef opencsv():\n    # 使用 pandas 打开\n    train_data = pd.read_csv(os.path.join(data_dir, 'input/train.csv'))\n    test_data = pd.read_csv(os.path.join(data_dir, 'input/test.csv'))\n    data = pd.concat([train_data, test_data], axis=0).reset_index(drop=True)\n    data.drop(['label'], axis=1, inplace=True)\n    label = train_data.label\n    return train_data, test_data, data, label\n\n\n# 数据预处理-降维 PCA主成成分分析\ndef dRPCA(data, COMPONENT_NUM=100):\n    print('dimensionality reduction...')\n    data = np.array(data)\n    '''\n    使用说明：https://www.cnblogs.com/pinard/p/6243025.html\n    n_components>=1\n      n_components=NUM  \b 设置\b占特征数量\n    0 < n_components < 1\n      n_components=0.99  \b设置阈值总方差占比\n    '''\n    pca = PCA(n_components=COMPONENT_NUM, random_state=34)\n    data_pca = pca.fit_transform(data)\n\n    # pca 方差大小、方差占比、特征数量\n    print(pca.explained_variance_, '\\n', pca.explained_variance_ratio_, '\\n',\n          pca.n_components_)\n    print(sum(pca.explained_variance_ratio_))\n    storeModel(data_pca, os.path.join(data_dir, 'output/Result_sklearn_rf.pcaData'))\n    return data_pca\n\n\n# 训练模型\ndef trainModel(X_train, y_train):\n    print('Train RF...')\n    clf = RandomForestClassifier(\n        n_estimators=10,\n        max_depth=10,\n        min_samples_split=2,\n        min_samples_leaf=1,\n        random_state=34)\n    clf.fit(X_train, y_train)  # 训练rf\n\n    # clf=LGBMClassifier(num_leaves=63, max_depth=7, n_estimators=80, n_jobs=20)\n\n    # param_test1 = {'n_estimators':arange(10,150,10),'max_depth':arange(1,21,1)}\n    # gsearch1 = GridSearchCV(estimator = clf, param_grid = param_test1, scoring='accuracy',iid=False,cv=5)\n    # gsearch1.fit(X_train, y_train)\n    # print(gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_)\n    # clf=gsearch1.best_estimator_\n    return clf\n\n\n# 计算准确率\ndef printAccuracy(y_test, y_predict):\n    zeroLable = y_test - y_predict\n    rightCount = 0\n    for i in range(len(zeroLable)):\n        if list(zeroLable)[i] == 0:\n            rightCount += 1\n    print('the right rate is:', float(rightCount) / len(zeroLable))\n\n\n# 存储模型\ndef storeModel(model, filename):\n    import pickle\n    with open(filename, 'wb') as fw:\n        pickle.dump(model, fw)\n\n\n# 加载模型\ndef getModel(filename):\n    import pickle\n    fr = open(filename, 'rb')\n    return pickle.load(fr)\n\n\n# 结果输出保存\ndef saveResult(result, csvName):\n    i = 0\n    n = len(result)\n    print('the size of test set is {}'.format(n))\n    with open(os.path.join(data_dir, 'output/Result_sklearn_RF.csv'), 'w') as fw:\n        fw.write('{},{}\\n'.format('ImageId', 'Label'))\n        for i in range(1, n + 1):\n            fw.write('{},{}\\n'.format(i, result[i - 1]))\n    print('Result saved successfully... and the path = {}'.format(csvName))\n\n\ndef trainRF():\n    start_time = time.time()\n    # 加载数据\n    train_data, test_data, data, label = opencsv()\n    print(\"load data finish\")\n    stop_time_l = time.time()\n    print('load data time used:%f s' % (stop_time_l - start_time))\n\n    startTime = time.time()\n    # 模型训练 (数据预处理-降维)\n    data_pca = dRPCA(data,100)\n\n    X_train, X_test, y_train, y_test = train_test_split(\n        data_pca[0:len(train_data)], label, test_size=0.1, random_state=34)\n\n    clf = trainModel(X_train, y_train)\n\n    # 保存结果\n    storeModel(data_pca[len(train_data):], os.path.join(data_dir, 'output/Result_sklearn_rf.pcaPreData'))\n    storeModel(clf, os.path.join(data_dir, 'output/Result_sklearn_rf.model'))\n\n    # 模型准确率\n    y_predict = clf.predict(X_test)\n    printAccuracy(y_test, y_predict)\n\n    print(\"finish!\")\n    stopTime = time.time()\n    print('TrainModel store time used:%f s' % (stopTime - startTime))\n\n\ndef preRF():\n    startTime = time.time()\n    # 加载模型和数据\n    clf = getModel(os.path.join(data_dir, 'output/Result_sklearn_rf.model'))\n    pcaPreData = getModel(os.path.join(data_dir, 'output/Result_sklearn_rf.pcaPreData'))\n\n    # 结果预测\n    result = clf.predict(pcaPreData)\n\n    # 结果的输出\n    saveResult(result, os.path.join(data_dir, 'output/Result_sklearn_rf.csv'))\n    print(\"finish!\")\n    stopTime = time.time()\n    print('PreModel load time used:%f s' % (stopTime - startTime))\n\n\nif __name__ == '__main__':\n\n    # 训练并保存模型\n    trainRF()\n\n    # 加载预测数据集\n    preRF()\n"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/digit-recognizer/svm-python3.6.py",
    "content": "#!/usr/bin/python3\n# coding: utf-8\n\n'''\nCreated on 2017-10-26\nUpdate  on 2017-10-26\nAuthor: 片刻\nGithub: https://github.com/apachecn/kaggle\n'''\n\nimport os\nimport csv\nimport time\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import train_test_split\n\n# 数据路径\ndata_dir = '/opt/data/kaggle/getting-started/digit-recognizer/'\n\n\n# 加载数据\ndef opencsv():\n    print('Load Data...')\n    # 使用 pandas 打开\n    dataTrain = pd.read_csv(os.path.join(data_dir, 'input/train.csv'))\n    dataPre = pd.read_csv(os.path.join(data_dir, 'input/test.csv'))\n    trainData = dataTrain.values[:, 1:]  # 读入全部训练数据\n    trainLabel = dataTrain.values[:, 0]\n    preData = dataPre.values[:, :]  # 测试全部测试个数据\n    return trainData, trainLabel, preData\n\n\n# 数据预处理-降维 PCA主成成分分析\ndef dRCsv(x_train, x_test, preData, COMPONENT_NUM):\n    print('dimensionality reduction...')\n    trainData = np.array(x_train)\n    testData = np.array(x_test)\n    preData = np.array(preData)\n\n    '''\n    使用说明：https://www.cnblogs.com/pinard/p/6243025.html\n    n_components>=1\n      n_components=NUM  \b 设置\b占特征数量比\n    0 < n_components < 1\n      n_components=0.99  \b设置阈值总方差占比\n    '''\n    pca = PCA(n_components=COMPONENT_NUM, whiten=True)\n    pca.fit(trainData)  # Fit the model with X\n    pcaTrainData = pca.transform(trainData)  # Fit the model with X and 在X上完成降维.\n    pcaTestData = pca.transform(testData)  # Fit the model with X and 在X上完成降维.\n    pcaPreData = pca.transform(preData)  # Fit the model with X and 在X上完成降维.\n\n    # pca 方差大小、方差占比、特征数量\n    print(pca.explained_variance_, '\\n', pca.explained_variance_ratio_, '\\n', pca.n_components_)\n    print(sum(pca.explained_variance_ratio_))\n    return pcaTrainData,  pcaTestData, pcaPreData\n\n\n# 训练模型\ndef trainModel(trainData, trainLabel):\n    print('Train SVM...')\n    clf = SVC(C=4, kernel='rbf')\n    clf.fit(trainData, trainLabel)  # 训练SVM\n    return clf\n\n\n# 结果输出保存\ndef saveResult(result, csvName):\n    with open(csvName, 'w') as myFile:\n        myWriter = csv.writer(myFile)\n        myWriter.writerow([\"ImageId\", \"Label\"])\n        index = 0\n        for r in result:\n            index += 1\n            myWriter.writerow([index, int(r)])\n    print('Saved successfully...')  # 保存预测结果\n\n\n# 分析数据,看数据是否满足要求（通过这些来检测数据的相关性，考虑在分类的时候提取出重要的特征）\ndef analyse_data(dataMat):\n    meanVals = np.mean(dataMat, axis=0)  # np.mean 求出每列的平均值meanVals\n    meanRemoved = dataMat-meanVals  # 每一列特征值减去该列的特征值均值\n    # 计算协方差矩阵，除数n-1是为了得到协方差的 无偏估计\n    # cov(X,0) = cov(X) 除数是n-1(n为样本个数)\n    # cov(X,1) 除数是n\n    covMat = np.cov(meanRemoved, rowvar=0)  # cov 计算协方差的值,\n    # np.mat 是用来生成一个矩阵的\n    # 保存特征值(eigvals)和对应的特征向量(eigVects)\n    eigvals, eigVects = np.linalg.eig(np.mat(covMat))  # linalg.eig 计算的值是矩阵的特征值，保存在对应的矩阵中\n    eigValInd = np.argsort(eigvals)  # argsort 对特征值进行排序，返回的是数值从小到大的索引值\n\n    topNfeat = 100  # 需要保留的特征维度，即要压缩成的维度数\n\n    # 从排序后的矩阵最后一个开始自下而上选取最大的N个特征值，返回其对应的索引\n    eigValInd = eigValInd[:-(topNfeat+1):-1]\n\n    # 计算特征值的总和\n    cov_all_score = float(sum(eigvals))\n    sum_cov_score = 0\n    for i in range(0, len(eigValInd)):\n        # 特征值进行相加\n        line_cov_score = float(eigvals[eigValInd[i]])\n        sum_cov_score += line_cov_score\n        '''\n        我们发现其中有超过20%的特征值都是0。\n        这就意味着这些特征都是其他特征的副本，也就是说，它们可以通过其他特征来表示，而本身并没有提供额外的信息。\n\n        最前面15个值的数量级大于10^5，实际上那以后的值都变得非常小。\n        这就相当于告诉我们只有部分重要特征，重要特征的数目也很快就会下降。\n\n        最后，我们可能会注意到有一些小的负值，他们主要源自数值误差应该四舍五入成0.\n        '''\n        print('主成分：%s, 方差占比：%s%%, 累积方差占比：%s%%' % (format(i+1, '2.0f'), format(line_cov_score/cov_all_score*100, '4.2f'), format(sum_cov_score/cov_all_score*100, '4.1f')))\n\n\n# 找出最高准确率\ndef getOptimalAccuracy(trainData, trainLabel, preData):\n    # 分析数据 100个特征左右\n    # analyse_data(trainData)\n    x_train, x_test, y_train, y_test = train_test_split(trainData, trainLabel, test_size=0.1)\n    lineLen, featureLen = np.shape(x_test) # shape 返回矩阵或者数值的长度\n    # print(lineLen, type(lineLen), featureLen, type(featureLen))\n\n    minErr = 1\n    minSumErr = 0\n    optimalNum = 1\n    optimalLabel = []\n    optimalSVMClf = None\n    pcaPreDataResult = None\n    for i in range(30, 45, 1):\n        # 评估训练结果\n        pcaTrainData,  pcaTestData, pcaPreData = dRCsv(x_train, x_test, preData, i)\n        clf = trainModel(pcaTrainData, y_train)\n        testLabel = clf.predict(pcaTestData)\n\n        errArr = np.mat(np.ones((lineLen, 1)))\n        sumErrArr = errArr[testLabel != y_test].sum()\n        sumErr = sumErrArr/lineLen\n\n        print('i=%s' % i, lineLen, sumErrArr, sumErr)\n        if sumErr <= minErr:\n            minErr = sumErr\n            minSumErr = sumErrArr\n            optimalNum = i\n            optimalSVMClf = clf\n            optimalLabel = testLabel\n            pcaPreDataResult = pcaPreData\n            print(\"i=%s >>>>> \\t\" % i, lineLen, int(minSumErr), 1-minErr)\n\n    '''\n    展现 准确率与召回率\n        precision 准确率\n        recall 召回率\n        f1-score  准确率和召回率的一个综合得分\n        support 参与比较的数量\n    参考链接：http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report\n    '''\n    # target_names 以 y的label分类为准\n    # target_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n    target_names = [str(i) for i in list(set(y_test))]\n    print(target_names)\n    print(classification_report(y_test, optimalLabel, target_names=target_names))\n    print(\"特征数量= %s, 存在最优解：>>> \\t\" % optimalNum, lineLen, int(minSumErr), 1-minErr)\n    return optimalSVMClf, pcaPreDataResult\n\n\n# 存储模型\ndef storeModel(model, filename):\n    import pickle\n    with open(filename, 'wb') as fw:\n        pickle.dump(model, fw)\n\n\n# 加载模型\ndef getModel(filename):\n    import pickle\n    fr = open(filename, 'rb')\n    return pickle.load(fr)\n\n\ndef trainDRSVM():\n    startTime = time.time()\n\n    # 加载数据\n    trainData, trainLabel, preData = opencsv()\n    # 模型训练 (数据预处理-降维)\n    optimalSVMClf, pcaPreData = getOptimalAccuracy(trainData, trainLabel, preData)\n\n    storeModel(optimalSVMClf, os.path.join(data_dir, 'output/Result_sklearn_SVM.model'))\n    storeModel(pcaPreData, os.path.join(data_dir, 'output/Result_sklearn_SVM.pcaPreData'))\n\n    print(\"finish!\")\n    stopTime = time.time()\n    print('TrainModel store time used:%f s' % (stopTime - startTime))\n\n\ndef preDRSVM():\n    startTime = time.time()\n    # 加载模型和数据\n    optimalSVMClf = getModel(os.path.join(data_dir, 'output/Result_sklearn_SVM.model'))\n    pcaPreData = getModel(os.path.join(data_dir, 'output/Result_sklearn_SVM.pcaPreData'))\n\n    # 结果预测\n    testLabel = optimalSVMClf.predict(pcaPreData)\n    # print(\"testLabel = %f\" % testscore)\n    # 结果的输出\n    saveResult(testLabel, os.path.join(data_dir, 'output/Result_sklearn_SVM.csv'))\n    print(\"finish!\")\n    stopTime = time.time()\n    print('PreModel load time used:%f s' % (stopTime - startTime))\n\n\n# 数据可视化\ndef dataVisulization(data, labels):\n    pca = PCA(n_components=2, whiten=True) # 使用PCA方法降到2维\n    pca.fit(data)\n    pcaData = pca.transform(data)\n    uniqueClasses = set(labels)\n    fig = plt.figure()\n    ax = fig.add_subplot(1, 1, 1)\n    for cClass in uniqueClasses:\n        plt.scatter(pcaData[labels==cClass, 0], pcaData[labels==cClass, 1])\n    plt.xlabel('$x_1$')\n    plt.ylabel('$x_2$')\n    plt.title('MNIST visualization')\n    plt.show()\n\n\nif __name__ == '__main__':\n    trainData, trainLabel, preData = opencsv()\n    dataVisulization(trainData, trainLabel)\n\n    # 训练并保存模型\n    # trainDRSVM()\n\n    # 分析数据\n    # analyse_data(trainData)\n    # 加载预测数据集\n    # preDRSVM()\n"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/house-prices/base-model_lasso_python3.6.py",
    "content": "#!/usr/bin/python\n# coding: utf-8\n'''\nCreated on 2017-12-11\nUpdate  on 2017-12-11\nAuthor: Usernametwo\nGithub: https://github.com/apachecn/kaggle\n'''\nimport time\nimport pandas as pd\nfrom sklearn.linear_model import Ridge\nimport os.path\n\ndata_dir = '/opt/data/kaggle/getting-started/house-prices'\n\n\n# 加载数据\ndef opencsv():\n    # 使用 pandas 打开\n    df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))\n    df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))\n\n    return df_train, df_test\n\n\ndef saveResult(result):\n    result.to_csv(\n        os.path.join(data_dir, \"submission.csv\"), sep=',', encoding='utf-8')\n\n\ndef ridgeRegression(trainData, trainLabel, df_test):\n    ridge = Ridge(\n        alpha=10.0\n    )  # default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10)\n    ridge.fit(trainData, trainLabel)\n    predict = ridge.predict(df_test)\n    pred_df = pd.DataFrame(predict, index=df_test[\"Id\"], columns=[\"SalePrice\"])\n    return pred_df\n\n\ndef dataProcess(df_train, df_test):\n    trainLabel = df_train['SalePrice']\n    df = pd.concat((df_train, df_test), axis=0, ignore_index=True)\n    df.dropna(axis=1, inplace=True)\n    df = pd.get_dummies(df)\n    trainData = df[:df_train.shape[0]]\n    test = df[df_train.shape[0]:]\n    return trainData, trainLabel, test\n\n\ndef Regression_ridge():\n    start_time = time.time()\n\n    # 加载数据\n    df_train, df_test = opencsv()\n\n    print(\"load data finish\")\n    stop_time_l = time.time()\n    print('load data time used:%f' % (stop_time_l - start_time))\n\n    # 数据预处理\n    train_data, trainLabel, df_test = dataProcess(df_train, df_test)\n\n    # 模型训练预测\n    result = ridgeRegression(train_data, trainLabel, df_test)\n\n    # 结果的输出\n    saveResult(result)\n    print(\"finish!\")\n    stop_time_r = time.time()\n    print('classify time used:%f' % (stop_time_r - start_time))\n\n\nif __name__ == '__main__':\n    Regression_ridge()\n"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/house-prices/deeplearning_method.py",
    "content": "# -*- coding: utf-8 -*-\n__author__ = 'liudong'\n__date__ = '2018/5/29 下午7:40'\nimport csv\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom tensorflow.python.framework import ops\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import preprocessing\n\ndef load_data(train_path, test_path):\n    \"\"\"\n    加载数据的方法\n    :param train_path: path for the train set file\n    :param test_path: path for the test set file\n    :return: a 'pandas' array for each set\n    \"\"\"\n\n    train_data = pd.read_csv(train_path)\n    test_data = pd.read_csv(test_path)\n\n    print(\"number of training examples = \" + str(train_data.shape[0])) # 1460\n    print(\"number of test examples = \" + str(test_data.shape[0])) # 1459\n    print(\"train shape: \" + str(train_data.shape)) # (1460, 81)\n    print(\"test shape: \" + str(test_data.shape)) #  (1459, 80)\n\n    return train_data, test_data\n\n\n\n\ndef pre_process_data(df):\n    \"\"\"\n    Perform a number of pre process functions on the data set\n    :param df: pandas data frame\n    :return: processed data frame\n    \"\"\"\n    # one-hot encode categorical values\n    df = pd.get_dummies(df)\n\n    return df\n\n\ndef mini_batches(train_set, train_labels, mini_batch_size):\n    \"\"\"\n    Generate mini batches from the data set (data and labels)\n    :param train_set: data set with the examples\n    :param train_labels: data set with the labels\n    :param mini_batch_size: mini batch size\n    :return: mini batches\n    \"\"\"\n    set_size = train_set.shape[0]\n    batches = []\n    num_complete_minibatches = set_size // mini_batch_size\n\n    for k in range(0, num_complete_minibatches):\n        mini_batch_x = train_set[k * mini_batch_size: (k + 1) * mini_batch_size]\n        mini_batch_y = train_labels[k * mini_batch_size: (k + 1) * mini_batch_size]\n        mini_batch = (mini_batch_x, mini_batch_y)\n        batches.append(mini_batch)\n\n    # Handling the end case (last mini-batch < mini_batch_size)\n    if set_size % mini_batch_size != 0:\n        mini_batch_x = train_set[(set_size - (set_size % mini_batch_size)):]\n        mini_batch_y = train_labels[(set_size - (set_size % mini_batch_size)):]\n        mini_batch = (mini_batch_x, mini_batch_y)\n        batches.append(mini_batch)\n\n    return batches\n\n\ndef create_placeholders(input_size, output_size):\n    \"\"\"\n    Creates the placeholders for the tensorflow session.\n    :param input_size: scalar, input size\n    :param output_size: scalar, output size\n    :return: X  placeholder for the data input, of shape [None, input_size] and dtype \"float\"\n    :return: Y placeholder for the input labels, of shape [None, output_size] and dtype \"float\"\n    \"\"\"\n\n    x = tf.placeholder(shape=(None, input_size), dtype=tf.float32, name=\"X\")\n    y = tf.placeholder(shape=(None, output_size), dtype=tf.float32, name=\"Y\")\n\n    return x, y\n\n\ndef forward_propagation(x, parameters, keep_prob=1.0, hidden_activation='relu'):\n    \"\"\"\n    Implement forward propagation with dropout for the [LINEAR->RELU]*(L-1)->LINEAR-> computation\n    :param x: data, pandas array of shape (input size, number of examples)\n    :param parameters: output of initialize_parameters()\n    :param keep_prob: probability to keep each node of the layer\n    :param hidden_activation: activation function of the hidden layers\n    :return: last LINEAR value\n    \"\"\"\n\n    a_dropout = x\n    n_layers = len(parameters) // 2  # number of layers in the neural network\n\n    for l in range(1, n_layers):\n        a_prev = a_dropout\n        a_dropout = linear_activation_forward(a_prev, parameters['w%s' % l], parameters['b%s' % l], hidden_activation)\n\n        if keep_prob < 1.0:\n            a_dropout = tf.nn.dropout(a_dropout, keep_prob)\n\n    al = tf.matmul(a_dropout, parameters['w%s' % n_layers]) + parameters['b%s' % n_layers]\n\n    return al\n\n\ndef linear_activation_forward(a_prev, w, b, activation):\n    \"\"\"\n    Implement the forward propagation for the LINEAR->ACTIVATION layer\n    :param a_prev: activations from previous layer (or input data): (size of previous layer, number of examples)\n    :param w: weights matrix: numpy array of shape (size of current layer, size of previous layer)\n    :param b: bias vector, numpy array of shape (size of the current layer, 1)\n    :param activation: the activation to be used in this layer, stored as a text string: \"sigmoid\" or \"relu\"\n    :return: the output of the activation function, also called the post-activation value\n    \"\"\"\n\n    a = None\n    if activation == \"sigmoid\":\n        z = tf.matmul(a_prev, w) + b\n        a = tf.nn.sigmoid(z)\n\n    elif activation == \"relu\":\n        z = tf.matmul(a_prev, w) + b\n        a = tf.nn.relu(z)\n\n    elif activation == \"leaky relu\":\n        z = tf.matmul(a_prev, w) + b\n        a = tf.nn.leaky_relu(z)\n\n    return a\n\n\ndef initialize_parameters(layer_dims):\n    \"\"\"\n    :param layer_dims: python array (list) containing the dimensions of each layer in our network\n    :return: python dictionary containing your parameters \"w1\", \"b1\", ..., \"wn\", \"bn\":\n                    Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])\n                    bl -- bias vector of shape (layer_dims[l], 1)\n    \"\"\"\n\n    parameters = {}\n    n_layers = len(layer_dims)\n\n    for l in range(1, n_layers):\n        parameters['w' + str(l)] = tf.get_variable('w' + str(l), [layer_dims[l - 1], layer_dims[l]],\n                                                   initializer=tf.contrib.layers.xavier_initializer())\n        parameters['b' + str(l)] = tf.get_variable('b' + str(l), [layer_dims[l]], initializer=tf.zeros_initializer())\n\n    return parameters\n\n\ndef compute_cost(z3, y):\n    \"\"\"\n    :param z3: output of forward propagation (output of the last LINEAR unit)\n    :param y: \"true\" labels vector placeholder, same shape as Z3\n    :return: Tensor of the cost function (RMSE as it is a regression)\n    \"\"\"\n\n    cost = tf.sqrt(tf.reduce_mean(tf.square(y - z3)))\n\n    return cost\n\n\ndef predict(data, parameters):\n    \"\"\"\n    make a prediction based on a data set and parameters\n    :param data: based data set\n    :param parameters: based parameters\n    :return: array of predictions\n    \"\"\"\n\n    init = tf.global_variables_initializer()\n    with tf.Session() as sess:\n        sess.run(init)\n\n        dataset = tf.cast(tf.constant(data), tf.float32)\n        fw_prop_result = forward_propagation(dataset, parameters)\n        prediction = fw_prop_result.eval()\n\n    return prediction\n\n\ndef rmse(predictions, labels):\n    \"\"\"\n    calculate cost between two data sets\n    :param predictions: data set of predictions\n    :param labels: data set of labels (real values)\n    :return: percentage of correct predictions\n    \"\"\"\n\n    prediction_size = predictions.shape[0]\n    prediction_cost = np.sqrt(np.sum(np.square(labels - predictions)) / prediction_size)\n\n    return prediction_cost\n\n\ndef rmsle(predictions, labels):\n    \"\"\"\n    calculate cost between two data sets\n    :param predictions: data set of predictions\n    :param labels: data set of labels (real values)\n    :return: percentage of correct predictions\n    \"\"\"\n\n    prediction_size = predictions.shape[0]\n    prediction_cost = np.sqrt(np.sum(np.square(np.log(predictions + 1) - np.log(labels + 1))) / prediction_size)\n\n    return prediction_cost\n\n\ndef l2_regularizer(cost, l2_beta, parameters, n_layers):\n    \"\"\"\n    Function to apply l2 regularization to the model\n    :param cost: usual cost of the model\n    :param l2_beta: beta value used for the normalization\n    :param parameters: parameters from the model (used to get weights values)\n    :param n_layers: number of layers of the model\n    :return: cost updated\n    \"\"\"\n\n    regularizer = 0\n    for i in range(1, n_layers):\n        regularizer += tf.nn.l2_loss(parameters['w%s' % i])\n\n    cost = tf.reduce_mean(cost + l2_beta * regularizer)\n\n    return cost\n\n\ndef build_submission_name(layers_dims, num_epochs, lr_decay,\n                          learning_rate, l2_beta, keep_prob, minibatch_size, num_examples):\n    \"\"\"\n    builds a string (submission file name), based on the model parameters\n    :param layers_dims: model layers dimensions\n    :param num_epochs: model number of epochs\n    :param lr_decay: model learning rate decay\n    :param learning_rate: model learning rate\n    :param l2_beta: beta used on l2 normalization\n    :param keep_prob: keep probability used on dropout normalization\n    :param minibatch_size: model mini batch size (0 to do not use mini batches)\n    :param num_examples: number of model examples (training data)\n    :return: built string\n    \"\"\"\n    submission_name = 'ly{}-epoch{}.csv' \\\n        .format(layers_dims, num_epochs)\n\n    if lr_decay != 0:\n        submission_name = 'lrdc{}-'.format(lr_decay) + submission_name\n    else:\n        submission_name = 'lr{}-'.format(learning_rate) + submission_name\n\n    if l2_beta > 0:\n        submission_name = 'l2{}-'.format(l2_beta) + submission_name\n\n    if keep_prob < 1:\n        submission_name = 'dk{}-'.format(keep_prob) + submission_name\n\n    if minibatch_size != num_examples:\n        submission_name = 'mb{}-'.format(minibatch_size) + submission_name\n\n    return submission_name\n\n\ndef plot_model_cost(train_costs, validation_costs, submission_name):\n    \"\"\"\n    :param train_costs: array with the costs from the model training\n    :param validation_costs: array with the costs from the model validation\n    :param submission_name: name of the submission (used for the plot title)\n    :return:\n    \"\"\"\n    plt.plot(np.squeeze(train_costs), label='Train cost')\n    plt.plot(np.squeeze(validation_costs), label='Validation cost')\n    plt.ylabel('cost')\n    plt.xlabel('iterations (per tens)')\n    plt.title(\"Model: \" + submission_name)\n    plt.legend()\n    plt.show()\n    plt.close()\n\n\ndef model(train_set, train_labels, validation_set, validation_labels, layers_dims, learning_rate=0.01, num_epochs=1001,\n          print_cost=True, plot_cost=True, l2_beta=0., keep_prob=1.0, hidden_activation='relu', return_best=False,\n          minibatch_size=0, lr_decay=0):\n    \"\"\"\n    :param train_set: training set\n    :param train_labels: training labels\n    :param validation_set: validation set\n    :param validation_labels: validation labels\n    :param layers_dims: array with the layer for the model\n    :param learning_rate: learning rate of the optimization\n    :param num_epochs: number of epochs of the optimization loop\n    :param print_cost: True to print the cost every 500 epochs\n    :param plot_cost: True to plot the train and validation cost\n    :param l2_beta: beta parameter for the l2 regularization\n    :param keep_prob: probability to keep each node of each hidden layer (dropout)\n    :param hidden_activation: activation function to be used on the hidden layers\n    :param return_best: True to return the highest params from all epochs\n    :param minibatch_size: size of th mini batch\n    :param lr_decay: if != 0, sets de learning rate decay on each epoch\n    :return parameters: parameters learnt by the model. They can then be used to predict.\n    :return submission_name: name for the trained model\n    \"\"\"\n\n    ops.reset_default_graph()  # to be able to rerun the model without overwriting tf variables\n\n    input_size = layers_dims[0]\n    output_size = layers_dims[-1]\n    num_examples = train_set.shape[0]\n    n_layers = len(layers_dims)\n    train_costs = []\n    validation_costs = []\n    best_iteration = [float('inf'), 0]\n    best_params = None\n\n    if minibatch_size == 0 or minibatch_size > num_examples:\n        minibatch_size = num_examples\n\n    num_minibatches = num_examples // minibatch_size\n\n    if num_minibatches == 0:\n        num_minibatches = 1\n\n    submission_name = build_submission_name(layers_dims, num_epochs, lr_decay, learning_rate, l2_beta, keep_prob,\n                                            minibatch_size, num_examples)\n\n    x, y = create_placeholders(input_size, output_size)\n    tf_valid_dataset = tf.cast(tf.constant(validation_set), tf.float32)\n    parameters = initialize_parameters(layers_dims)\n\n    fw_output_train = forward_propagation(x, parameters, keep_prob, hidden_activation)\n    train_cost = compute_cost(fw_output_train, y)\n\n    fw_output_valid = forward_propagation(tf_valid_dataset, parameters, keep_prob, hidden_activation)\n    validation_cost = compute_cost(fw_output_valid, validation_labels)\n\n    if l2_beta > 0:\n        train_cost = l2_regularizer(train_cost, l2_beta, parameters, n_layers)\n        validation_cost = l2_regularizer(validation_cost, l2_beta, parameters, n_layers)\n\n    if lr_decay != 0:\n        global_step = tf.Variable(0, trainable=False)\n        learning_rate = tf.train.inverse_time_decay(learning_rate, global_step=global_step, decay_rate=lr_decay,\n                                                    decay_steps=1)\n        optimizer = tf.train.AdamOptimizer(learning_rate).minimize(train_cost, global_step=global_step)\n    else:\n        optimizer = tf.train.AdamOptimizer(learning_rate).minimize(train_cost)\n\n    # uncomment to use tensorboard\n    # tf.summary.scalar('train cost', train_cost)\n    # tf.summary.scalar('validation cost', validation_cost)\n\n    init = tf.global_variables_initializer()\n\n    with tf.Session() as sess:\n        # uncomment to use tensorboard\n        # writer = tf.summary.FileWriter('logs/'+submission_name, sess.graph)\n\n        sess.run(init)\n\n        for epoch in range(num_epochs):\n            train_epoch_cost = 0.\n            validation_epoch_cost = 0.\n\n            minibatches = mini_batches(train_set, train_labels, minibatch_size)\n\n            for minibatch in minibatches:\n                # uncomment to use tensorboard\n                # merge = tf.summary.merge_all()\n\n                (minibatch_X, minibatch_Y) = minibatch\n                feed_dict = {x: minibatch_X, y: minibatch_Y}\n\n                # uncomment to use tensorboard\n                # _, summary, minibatch_train_cost, minibatch_validation_cost = sess.run(\n                #     [optimizer, merge, train_cost, validation_cost], feed_dict=feed_dict)\n\n                # comment to use tensorboard\n                _, minibatch_train_cost, minibatch_validation_cost = sess.run(\n                    [optimizer, train_cost, validation_cost], feed_dict=feed_dict)\n\n                train_epoch_cost += minibatch_train_cost / num_minibatches\n                validation_epoch_cost += minibatch_validation_cost / num_minibatches\n\n            if print_cost is True and epoch % 500 == 0:\n                print(\"Train cost after epoch %i: %f\" % (epoch, train_epoch_cost))\n                print(\"Validation cost after epoch %i: %f\" % (epoch, validation_epoch_cost))\n\n            if plot_cost is True and epoch % 10 == 0:\n                train_costs.append(train_epoch_cost)\n                validation_costs.append(validation_epoch_cost)\n\n            # uncomment to use tensorboard\n            # if epoch % 10 == 0:\n            #     writer.add_summary(summary, epoch)\n\n            if return_best is True and validation_epoch_cost < best_iteration[0]:\n                best_iteration[0] = validation_epoch_cost\n                best_iteration[1] = epoch\n                best_params = sess.run(parameters)\n\n        if return_best is True:\n            parameters = best_params\n        else:\n            parameters = sess.run(parameters)\n\n        print(\"Parameters have been trained, getting metrics...\")\n\n        train_rmse = rmse(predict(train_set, parameters), train_labels)\n        validation_rmse = rmse(predict(validation_set, parameters), validation_labels)\n        train_rmsle = rmsle(predict(train_set, parameters), train_labels)\n        validation_rmsle = rmsle(predict(validation_set, parameters), validation_labels)\n\n        print('Train rmse: {:.4f}'.format(train_rmse))\n        print('Validation rmse: {:.4f}'.format(validation_rmse))\n        print('Train rmsle: {:.4f}'.format(train_rmsle))\n        print('Validation rmsle: {:.4f}'.format(validation_rmsle))\n\n        submission_name = 'tr_cost-{:.2f}-vd_cost{:.2f}-'.format(train_rmse, validation_rmse) + submission_name\n\n        if return_best is True:\n            print('Lowest rmse: {:.2f} at epoch {}'.format(best_iteration[0], best_iteration[1]))\n\n        if plot_cost is True:\n            plot_model_cost(train_costs, validation_costs, submission_name)\n\n        return parameters, submission_name\n\nTRAIN_PATH = '/Users/liudong/Desktop/house_price/train.csv'\nTEST_PATH = '/Users/liudong/Desktop/house_price/test.csv'\n\ntrain, test = load_data(TRAIN_PATH, TEST_PATH)\n\n# get the labels values\ntrain_raw_labels = train['SalePrice'].to_frame().as_matrix()\n\n# pre process data sets\ntrain_pre = pre_process_data(train)\ntest_pre = pre_process_data(test)\n\n# drop unwanted columns\ntrain_pre = train_pre.drop(['Id', 'SalePrice'], axis=1)\ntest_pre = test_pre.drop(['Id'], axis=1)\n\n# align both data sets (by outer join), to make they have the same amount of features,\n# this is required because of the mismatched categorical values in train and test sets\ntrain_pre, test_pre = train_pre.align(test_pre, join='outer', axis=1)\n\n# replace the nan values added by align for 0\ntrain_pre.replace(to_replace=np.nan, value=0, inplace=True)\ntest_pre.replace(to_replace=np.nan, value=0, inplace=True)\n\ntrain_pre = train_pre.as_matrix().astype(np.float)\ntest_pre = test_pre.as_matrix().astype(np.float)\n\n# scale values\nstandard_scaler = preprocessing.StandardScaler()\ntrain_pre = standard_scaler.fit_transform(train_pre)\ntest_pre = standard_scaler.fit_transform(test_pre)\n\nX_train, X_valid, Y_train, Y_valid = train_test_split(train_pre, train_raw_labels, test_size=0.3, random_state=1)\n\n# 模型的超参数设置\ninput_size = train_pre.shape[1]\noutput_size = 1\nnum_epochs = 10000\nlearning_rate = 0.01\nlayers_dims = [input_size, 500, 500, output_size]\nparameters, submission_name = model(X_train, Y_train, X_valid, Y_valid, layers_dims, num_epochs=num_epochs,\n                                    learning_rate=learning_rate, print_cost=True, plot_cost=True, l2_beta=10,\n                                    keep_prob=0.7, minibatch_size=0, return_best=True)\n\nprint(submission_name)\nprediction = list(map(lambda val: float(val), predict(test_pre, parameters)))\n# uncomment if label was log transformed\n# prediction = list(map(lambda val: np.expm1(val), prediction))\n# output_submission(test.Id.values, prediction, 'Id', 'SalePrice', submission_name)\n# 保存结果\nresult = pd.DataFrame()\nresult['Id'] = test.Id.values\nresult['SalePrice'] = prediction\n# index=False 是用来除去行编号\nresult.to_csv('/Users/liudong/Desktop/house_price/result1.csv', index=False)\nprint('##########结束训练##########')"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/house-prices/housePredice_335.py",
    "content": "# -*- coding: utf-8 -*-\n__author__ = 'liudong'\n__date__ = '2018/4/23 下午2:28'\n#import some necessary librairies\n\nimport numpy as np  # linear algebra\nimport pandas as pd  # data processing, CSV file I/O (e.g. pd.read_csv)\n# %matplotlib inline\nimport matplotlib.pyplot as plt  # Matlab-style plotting\nimport seaborn as sns\ncolor = sns.color_palette()\nsns.set_style('darkgrid')\nimport warnings\n\n\ndef ignore_warn(*args, **kwargs):\n    pass\n\n\n# ignore annoying warning (from sklearn and seaborn)\nwarnings.warn = ignore_warn\nfrom sklearn.preprocessing import LabelEncoder\nfrom scipy import stats\nfrom scipy.stats import norm, skew  #for some statistics\nfrom sklearn.linear_model import ElasticNet, Lasso, BayesianRidge, LassoLarsIC\nfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\nfrom sklearn.kernel_ridge import KernelRidge\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\nfrom sklearn.model_selection import KFold, cross_val_score, train_test_split\nfrom sklearn.metrics import mean_squared_error\nimport xgboost as xgb\nimport lightgbm as lgb\n\n# Limiting floats output to 3 decimal points\npd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))\n\nfrom subprocess import check_output\n# check the files available in the directory\n# print(check_output([\"ls\", \"/Users/liudong/Desktop/house_price/train.csv\"]).decode(\"utf8\"))\n# 加载数据\ntrain = pd.read_csv('/opt/data/kaggle/getting-started/house-prices/train.csv')\ntest = pd.read_csv('/opt/data/kaggle/getting-started/house-prices/test.csv')\n# 查看训练数据的特征\nprint(train.head(5))\n# 查看测试数据的特征\nprint(test.head(5))\n\n# 查看数据的数量和特征值的个数\nprint(\"The train data size before dropping Id feature is : {} \".format(\n    train.shape))\nprint(\"The test data size before dropping Id feature is : {} \".format(\n    test.shape))\n\n# Save the 'Id' colum\ntrain_ID = train['Id']\ntest_ID = test['Id']\n\n# Now drop the  'Id' colum since it's unnecessary for  the prediction process.\ntrain.drop(\"Id\", axis=1, inplace=True)\ntest.drop(\"Id\", axis=1, inplace=True)\n\n#check again the data size after dropping the 'Id' variable\nprint(\"\\nThe train data size after dropping Id feature is : {} \".format(\n    train.shape))\nprint(\n    \"The test data size after dropping Id feature is : {} \".format(test.shape))\n\n# 删除那些异常数据值\ntrain = train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<300000)].index)\n\n# We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n# log(1+x)来处理所有的数值\ntrain[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n\n# 特征工程\n# 把训练集和测试集的数据contact一起放置在DataFrame当中\n# 0 代表行数\nntrain = train.shape[0] \nntest = test.shape[0]\n# SalesPrice的值\ny_train = train.SalePrice.values\nall_data = pd.concat((train, test)).reset_index(drop=True)\nall_data.drop(['SalePrice'], axis=1, inplace=True)\nprint(\"all_data size is : {}\".format(all_data.shape))\n\n# 处理缺失数据\nall_data_na = (all_data.isnull().sum() / len(all_data)) * 100\nall_data_na = all_data_na.drop(\n    all_data_na[all_data_na == 0].index).sort_values(ascending=False)[:30]\nmissing_data = pd.DataFrame({'Missing Ratio': all_data_na})\nprint(missing_data.head(20))\n\nall_data[\"PoolQC\"] = all_data[\"PoolQC\"].fillna(\"None\")\nall_data[\"MiscFeature\"] = all_data[\"MiscFeature\"].fillna(\"None\")\nall_data[\"Alley\"] = all_data[\"Alley\"].fillna(\"None\")\nall_data[\"Fence\"] = all_data[\"Fence\"].fillna(\"None\")\nall_data[\"FireplaceQu\"] = all_data[\"FireplaceQu\"].fillna(\"None\")\n# 通过neighborhood进行分组，同时使用median来填充缺失数据\nall_data[\"LotFrontage\"] = all_data.groupby(\"Neighborhood\")[\"LotFrontage\"].transform(\n    lambda x: x.fillna(x.median()))\n# 根据数值的类型不同，选择不同的填充值\n# 使用None来填充缺失值 fillna('None') \nfor col in ('GarageType', 'GarageFinish', 'GarageQual', 'GarageCond'):\n    all_data[col] = all_data[col].fillna('None')\n# 使用0来填充缺失值 fillna(0)\nfor col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):\n    all_data[col] = all_data[col].fillna(0)\nfor col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF',\n            'BsmtFullBath', 'BsmtHalfBath'):\n    all_data[col] = all_data[col].fillna(0)\nfor col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1',\n            'BsmtFinType2'):\n    all_data[col] = all_data[col].fillna('None')\nall_data[\"MasVnrType\"] = all_data[\"MasVnrType\"].fillna(\"None\")\nall_data[\"MasVnrArea\"] = all_data[\"MasVnrArea\"].fillna(0)\nall_data['MSZoning'] = all_data['MSZoning'].fillna(\n    all_data['MSZoning'].mode()[0])\nall_data = all_data.drop(['Utilities'], axis=1)\nall_data[\"Functional\"] = all_data[\"Functional\"].fillna(\"Typ\")\n# mode()  [0]对行取众数 [1]是对列取众数(这里没用到）\nall_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0])\nall_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0])\nall_data['Exterior1st'] = all_data['Exterior1st'].fillna(all_data['Exterior1st'].mode()[0])\nall_data['Exterior2nd'] = all_data['Exterior2nd'].fillna(all_data['Exterior2nd'].mode()[0])\nall_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0])\nall_data['MSSubClass'] = all_data['MSSubClass'].fillna(\"None\")\n#检查数值是否还有缺失\nall_data_na = (all_data.isnull().sum() / len(all_data)) * 100\nall_data_na = all_data_na.drop(\n    all_data_na[all_data_na == 0].index).sort_values(ascending=False)\nmissing_data = pd.DataFrame({'Missing Ratio': all_data_na})\nprint(missing_data.head())\n# 另外的特征工程\n# Transforming some numerical variables that are really categorical\n# MSSubClass=The building class\nall_data['MSSubClass'] = all_data['MSSubClass'].apply(str)\n\n# Changing OverallCond into a categorical variable\nall_data['OverallCond'] = all_data['OverallCond'].astype(str)\n\n# Year and month sold are transformed into categorical features.\nall_data['YrSold'] = all_data['YrSold'].astype(str)\nall_data['MoSold'] = all_data['MoSold'].astype(str)\n\ncols = ('FireplaceQu', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageCond',\n        'ExterQual', 'ExterCond','HeatingQC', 'PoolQC', 'KitchenQual', 'BsmtFinType1',\n        'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope',\n        'LotShape', 'PavedDrive', 'Street', 'Alley', 'CentralAir', 'MSSubClass', 'OverallCond',\n        'YrSold', 'MoSold')\n# 使用 LabelEncoder 转换上述特征\nfor c in cols:\n    lbl = LabelEncoder()\n    lbl.fit(list(all_data[c].values))\n    all_data[c] = lbl.transform(list(all_data[c].values))\n\n# shape\nprint('Shape all_data: {}'.format(all_data.shape))\n\n# 增加更多重要的特征\n# Adding total sqfootage feature\nall_data['TotalSF'] = all_data['TotalBsmtSF'] + all_data[\n    '1stFlrSF'] + all_data['2ndFlrSF']\n# Skewed features\nnumeric_feats = all_data.dtypes[all_data.dtypes != \"object\"].index\n\n# Check the skew of all numerical features\nskewed_feats = all_data[numeric_feats].apply(\n    lambda x: skew(x.dropna())).sort_values(ascending=False)\nprint(\"\\nSkew in numerical features: \\n\")\nskewness = pd.DataFrame({'Skew': skewed_feats})\nprint(skewness.head(10))\n\n# Box Cox Transformation of (highly) skewed features\n# We use the scipy function boxcox1p which computes the Box-Cox transformation of  1+x .\n# Note that setting  λ=0  is equivalent to log1p used above for the target variable.\nskewness = skewness[abs(skewness) > 0.75]\nprint(\"There are {} skewed numerical features to Box Cox transform\".format(\n    skewness.shape[0]))\n\nfrom scipy.special import boxcox1p\n\nskewed_features = skewness.index\nlam = 0.15\nfor feat in skewed_features:\n    # all_data[feat] += 1\n    all_data[feat] = boxcox1p(all_data[feat], lam)\n# Getting dummy categorical features\nall_data = pd.get_dummies(all_data)\nprint(all_data.shape)\n# Getting the new train and test sets.\ntrain = all_data[:ntrain]\ntest = all_data[ntrain:]\n\n#Validation function\nn_folds = 5\n\n\ndef rmsle_cv(model):\n    kf = KFold(\n        n_folds, shuffle=True, random_state=42).get_n_splits(train.values)\n    rmse = np.sqrt(-cross_val_score(\n        model, train.values, y_train, scoring=\"neg_mean_squared_error\", cv=kf))\n    print(\"rmse\", rmse)\n    return (rmse)\n\n\n# 模型\n# LASSO Regression :\nlasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=1))\n# Elastic Net Regression\nENet = make_pipeline(\n    RobustScaler(), ElasticNet(\n        alpha=0.0005, l1_ratio=.9, random_state=3))\n# Kernel Ridge Regression\nKRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)\n# Gradient Boosting Regression\nGBoost = GradientBoostingRegressor(\n    n_estimators=3000,\n    learning_rate=0.05,\n    max_depth=4,\n    max_features='sqrt',\n    min_samples_leaf=15,\n    min_samples_split=10,\n    loss='huber',\n    random_state=5)\n#  XGboost\nmodel_xgb = xgb.XGBRegressor(\n    colsample_bytree=0.4603,\n    gamma=0.0468,\n    learning_rate=0.05,\n    max_depth=3,\n    min_child_weight=1.7817,\n    n_estimators=2200,\n    reg_alpha=0.4640,\n    reg_lambda=0.8571,\n    subsample=0.5213,\n    silent=1,\n    random_state=7,\n    nthread=-1)\n# lightGBM\nmodel_lgb = lgb.LGBMRegressor(\n    objective='regression',\n    num_leaves=5,\n    learning_rate=0.05,\n    n_estimators=720,\n    max_bin=55,\n    bagging_fraction=0.8,\n    bagging_freq=5,\n    feature_fraction=0.2319,\n    feature_fraction_seed=9,\n    bagging_seed=9,\n    min_data_in_leaf=6,\n    min_sum_hessian_in_leaf=11)\n# Base models scores\nscore = rmsle_cv(lasso)\nprint(\"\\nLasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\nscore = rmsle_cv(ENet)\nprint(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\nscore = rmsle_cv(KRR)\nprint(\n    \"Kernel Ridge score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\nscore = rmsle_cv(GBoost)\nprint(\"Gradient Boosting score: {:.4f} ({:.4f})\\n\".format(score.mean(),\n                                                          score.std()))\nscore = rmsle_cv(model_xgb)\nprint(\"Xgboost score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\nscore = rmsle_cv(model_lgb)\nprint(\"LGBM score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n\n\n# 模型融合\nclass AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):\n    def __init__(self, models):\n        self.models = models\n\n    # we define clones of the original models to fit the data in\n    def fit(self, X, y):\n        self.models_ = [clone(x) for x in self.models]\n\n        # Train cloned base models\n        for model in self.models_:\n            model.fit(X, y)\n\n        return self\n\n    # Now we do the predictions for cloned models and average them\n    def predict(self, X):\n        predictions = np.column_stack(\n            [model.predict(X) for model in self.models_])\n        return np.mean(predictions, axis=1)\n\n\n# 评价这四个模型的好坏\naveraged_models = AveragingModels(models=(ENet, GBoost, KRR, lasso))\nscore = rmsle_cv(averaged_models)\nprint(\" Averaged base models score: {:.4f} ({:.4f})\\n\".format(score.mean(),\n                                                              score.std()))\n\n\nclass StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin):\n    def __init__(self, base_models, meta_model, n_folds=5):\n        self.base_models = base_models\n        self.meta_model = meta_model\n        self.n_folds = n_folds\n\n    # We again fit the data on clones of the original models\n    def fit(self, X, y):\n        self.base_models_ = [list() for x in self.base_models]\n        self.meta_model_ = clone(self.meta_model)\n        kfold = KFold(n_splits=self.n_folds, shuffle=True, random_state=156)\n\n        # Train cloned base models then create out-of-fold predictions\n        # that are needed to train the cloned meta-model\n        out_of_fold_predictions = np.zeros((X.shape[0], len(self.base_models)))\n        for i, model in enumerate(self.base_models):\n            for train_index, holdout_index in kfold.split(X, y):\n                instance = clone(model)\n                self.base_models_[i].append(instance)\n                instance.fit(X[train_index], y[train_index])\n                y_pred = instance.predict(X[holdout_index])\n                out_of_fold_predictions[holdout_index, i] = y_pred\n\n        # Now train the cloned  meta-model using the out-of-fold predictions as new feature\n        self.meta_model_.fit(out_of_fold_predictions, y)\n        return self\n\n    # Do the predictions of all base models on the test data and use the averaged predictions as\n    # meta-features for the final prediction which is done by the meta-model\n    def predict(self, X):\n        meta_features = np.column_stack([\n            np.column_stack([model.predict(X) for model in base_models]).mean(\n                axis=1) for base_models in self.base_models_\n        ])\n        return self.meta_model_.predict(meta_features)\n\n\nstacked_averaged_models = StackingAveragedModels(\n    base_models=(ENet, GBoost, KRR), meta_model=lasso)\nscore = rmsle_cv(stacked_averaged_models)\nprint(\"Stacking Averaged models score: {:.4f} ({:.4f})\".format(score.mean(),\n                                                               score.std()))\n\n\n# define a rmsle evaluation function\ndef rmsle(y, y_pred):\n    return np.sqrt(mean_squared_error(y, y_pred))\n\n\n# Final Training and Prediction\n# StackedRegressor\nstacked_averaged_models.fit(train.values, y_train)\nstacked_train_pred = stacked_averaged_models.predict(train.values)\nstacked_pred = np.expm1(stacked_averaged_models.predict(test.values))\nprint(rmsle(y_train, stacked_train_pred))\n\n# XGBoost\nmodel_xgb.fit(train, y_train)\nxgb_train_pred = model_xgb.predict(train)\nxgb_pred = np.expm1(model_xgb.predict(test))\nprint(rmsle(y_train, xgb_train_pred))\n# lightGBM\nmodel_lgb.fit(train, y_train)\nlgb_train_pred = model_lgb.predict(train)\nlgb_pred = np.expm1(model_lgb.predict(test.values))\nprint(rmsle(y_train, lgb_train_pred))\n'''RMSE on the entire Train data when averaging'''\n\nprint('RMSLE score on train data:')\nprint(rmsle(y_train, stacked_train_pred * 0.70 + xgb_train_pred * 0.15 +\n            lgb_train_pred * 0.15))\n# 模型融合的预测效果\nensemble = stacked_pred * 0.70 + xgb_pred * 0.15 + lgb_pred * 0.15\n# 保存结果\nresult = pd.DataFrame()\nresult['Id'] = test_ID\nresult['SalePrice'] = ensemble\n# index=False 是用来除去行编号\nresult.to_csv('/Users/liudong/Desktop/house_price/result.csv', index=False)\nprint('##########结束训练##########')\n"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/house-prices/jiangheng_houseprice.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Dec 16 17:12:06 2017\n\n@author: Administrator\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nfrom pandas import DataFrame, Series\nfrom scipy.stats import boxcox\nfrom sklearn.linear_model import Ridge\nimport warnings\nimport os.path\n\nwarnings.filterwarnings('ignore')\n\ndata_dir = '/opt/data/kaggle/getting-started/house-prices'\n\n# 这里对数据做一些转换,原因要么是某些类别个数太少而且分布相近,要么是特征内的值之间有较为明显的优先级\nmapper = {\n    'LandSlope': {\n        'Gtl': 'Gtl',\n        'Mod': 'unGtl',\n        'Sev': 'unGtl'\n    },\n    'LotShape': {\n        'Reg': 'Reg',\n        'IR1': 'IR1',\n        'IR2': 'other',\n        'IR3': 'other'\n    },\n    'RoofMatl': {\n        'ClyTile': 'other',\n        'CompShg': 'CompShg',\n        'Membran': 'other',\n        'Metal': 'other',\n        'Roll': 'other',\n        'Tar&Grv': 'Tar&Grv',\n        'WdShake': 'WdShake',\n        'WdShngl': 'WdShngl'\n    },\n    'Heating': {\n        'GasA': 'GasA',\n        'GasW': 'GasW',\n        'Grav': 'Grav',\n        'Floor': 'other',\n        'OthW': 'other',\n        'Wall': 'Wall'\n    },\n    'HeatingQC': {\n        'Po': 1,\n        'Fa': 2,\n        'TA': 3,\n        'Gd': 4,\n        'Ex': 5\n    },\n    'KitchenQual': {\n        'Fa': 1,\n        'TA': 2,\n        'Gd': 3,\n        'Ex': 4\n    }\n}\n\n# 对结果影响很小,或者与其他特征相关性较高的特征将被丢弃\nto_drop = [\n    'Id', 'Street', 'Utilities', 'Condition2', 'PoolArea', 'PoolQC', 'Fence',\n    'YrSold', 'MoSold', 'BsmtHalfBath', 'BsmtFinSF2', 'GarageQual', 'MiscVal',\n    'EnclosedPorch', '3SsnPorch', 'GarageArea', 'TotRmsAbvGrd', 'GarageYrBlt',\n    'BsmtFinType2', 'BsmtUnfSF', 'GarageCond', 'GarageFinish', 'FireplaceQu',\n    'BsmtCond', 'BsmtQual', 'Alley'\n]\n\n# 特渣工程之瞎搞特征,别问我思路是什么,纯属乱拍脑袋搞出来,而且对结果貌似也仅有一点点影响\n'''\ndata['house_remod']:  重新装修的年份与房建年份的差值\ndata['livingRate']:   LotArea查了下是地块面积,这个特征是居住面积/地块面积*总体评价\ndata['lot_area']:    LotFrontage是与房子相连的街道大小,现在想了下把GrLivArea换成LotArea会不会好点?\ndata['room_area']:   房间数/居住面积\ndata['fu_room']:    带有浴室的房间占总房间数的比例\ndata['gr_room']:    卧室与房间数的占比\n'''\n\n\ndef create_feature(data):\n    # 是否拥有地下室\n    hBsmt_index = data.index[data['TotalBsmtSF'] > 0]\n    data['HaveBsmt'] = 0\n    data.loc[hBsmt_index, 'HaveBsmt'] = 1\n    data['house_remod'] = data['YearRemodAdd'] - data['YearBuilt']\n    data['livingRate'] = (data['GrLivArea'] /\n                          data['LotArea']) * data['OverallCond']\n    data['lot_area'] = data['LotFrontage'] / data['GrLivArea']\n    data['room_area'] = data['TotRmsAbvGrd'] / data['GrLivArea']\n    data['fu_room'] = data['FullBath'] / data['TotRmsAbvGrd']\n    data['gr_room'] = data['BedroomAbvGr'] / data['TotRmsAbvGrd']\n\n\ndef processing(data):\n    # 构造新特征\n    create_feature(data)\n    # 丢弃特征\n    data.drop(to_drop, axis=1, inplace=True)\n\n    # 填充None值,因为在特征说明中,None也是某些特征的一个值,所以对于这部分特征的缺失值以None填充\n    fill_none = ['MasVnrType', 'BsmtExposure', 'GarageType', 'MiscFeature']\n    for col in fill_none:\n        data[col].fillna('None', inplace=True)\n\n    # 对其他缺失值进行填充,离散型特征填充众数,数值型特征填充中位数\n    na_col = data.dtypes[data.isnull().any()]\n    for col in na_col.index:\n        if na_col[col] != 'object':\n            med = data[col].median()\n            data[col].fillna(med, inplace=True)\n        else:\n            mode = data[col].mode()[0]\n            data[col].fillna(mode, inplace=True)\n\n    # 对正态偏移的特征进行正态转换,numeric_col就是数值型特征,zero_col是含有零值的数值型特征\n    # 因为如果对含零特征进行转换的话会有各种各种的小问题,所以干脆单独只对非零数值进行转换\n    numeric_col = data.skew().index\n    zero_col = data.columns[data.isin([0]).any()]\n    for col in numeric_col:\n        # 对于那些condition特征,例如取值是0,1,2,3...那些我不作变换,因为意义不大\n        if len(pd.value_counts(data[col])) <= 10: continue\n        # 如果是含有零值的特征,则只对非零值变换,至于用哪种形式变换,boxcox会自动根据数据来调整\n        if col in zero_col:\n            trans_data = data[data > 0][col]\n            before = abs(trans_data.skew())\n            cox, _ = boxcox(trans_data)\n            log_after = abs(Series(cox).skew())\n            if log_after < before:\n                data.loc[trans_data.index, col] = cox\n        # 如果是非零值的特征,则全部作转换\n        else:\n            before = abs(data[col].skew())\n            cox, _ = boxcox(data[col])\n            log_after = abs(Series(cox).skew())\n            if log_after < before:\n                data.loc[:, col] = cox\n    # mapper值的映射转换\n    for col, mapp in mapper.items():\n        data.loc[:, col] = data[col].map(mapp)\n\n\ndf_train = pd.read_csv(os.path.join(data_dir, \"train.csv\"))\ndf_test = pd.read_csv(os.path.join(data_dir, \"test.csv\"))\ntest_ID = df_test['Id']\n\n# 去除离群点\nGrLivArea_outlier = set(df_train.index[(df_train['SalePrice'] < 200000) & (\n    df_train['GrLivArea'] > 4000)])\nLotFrontage_outlier = set(df_train.index[df_train['LotFrontage'] > 300])\ndf_train.drop(LotFrontage_outlier | GrLivArea_outlier, inplace=True)\n\n# 因为删除了几行数据,所以index的序列不再连续,需要重新reindex\ndf_train.reset_index(drop=True, inplace=True)\nprices = np.log1p(df_train.loc[:, 'SalePrice'])\ndf_train.drop(['SalePrice'], axis=1, inplace=True)\n# 这里对训练集和测试集进行合并,然后再进行特征工程\nall_data = pd.concat([df_train, df_test])\nall_data.reset_index(drop=True, inplace=True)\n\n# 进行特征工程\nprocessing(all_data)\n\n# dummy转换\ndummy = pd.get_dummies(all_data, drop_first=True)\n\n# 试了Ridge,Lasso,ElasticNet以及GBM,发现ridge的表现比其他的都好,参数alpha=6是调参结果\nridge = Ridge(6)\nridge.fit(dummy.iloc[:prices.shape[0], :], prices)\nresult = np.expm1(ridge.predict(dummy.iloc[prices.shape[0]:, :]))\npre = DataFrame(result, columns=['SalePrice'])\nprediction = pd.concat([test_ID, pre], axis=1)\nprediction.to_csv(os.path.join(data_dir, \"submission_1.csv\"), index=False)\n"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/titanic/introduction-to-ensemblingStacking-in-python.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 2017-12-4 15:45:02\n@Team: 瑶瑶亲卫队\n\"\"\"\n\n\n# 加载用到的库\nimport pandas as pd\nimport numpy as np\nimport re\nimport sklearn\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport csv\n\nimport plotly.offline as py\npy.init_notebook_mode(connected=True)\nimport plotly.graph_objs as go\nimport plotly.tools as tls\n\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# 打算使用 5 种 sklearn 中的模型来拟合\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.cross_validation import KFold\n\n# 加载最初的 train 和 test 数据集\ntrain = pd.read_csv('D:/titanic/titanic_dataset/train.csv')\ntest = pd.read_csv('D:/titanic/titanic_dataset/test.csv')\n\n# 存储 test 数据集的PassengerId 以便后面生成 submission 文件\nPassengerId = test['PassengerId']\n\n# train.head(3)\n\n# 整体的全部数据\nfull_data = [train, test]\n\n# 一些从原始 features 中衍生出的 features ，我认为可以算是一个 feature\n# Name_length 特征\ntrain['Name_length'] = train['Name'].apply(len)\ntest['Name_length'] = test['Name'].apply(len)\n# 在 Titanic 上是否具有一个 cabin\ntrain['Has_Cabin'] = train[\"Cabin\"].apply(lambda x: 0 if type(x) == float else 1)\ntest['Has_Cabin'] = test[\"Cabin\"].apply(lambda x: 0 if type(x) == float else 1)\n\n# 创建一个新的 feature FamilySize 作为 SibSp 和 Parch 的混合 features\nfor dataset in full_data:\n    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n# 从 FamilySize 特征中 创建一个新的 feature IsAlone \nfor dataset in full_data:\n    dataset['IsAlone'] = 0\n    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n# 删除 Embarked 列中的所有的 NULLS值，使用 S 来填充（算是超级暴力的吧）\nfor dataset in full_data:\n    dataset['Embarked'] = dataset['Embarked'].fillna('S')\n# 删除 Fare 中所有的 NULLS 值，并生成一个新的 feature 列 CategoricalFare\nfor dataset in full_data:\n    dataset['Fare'] = dataset['Fare'].fillna(train['Fare'].median())\ntrain['CategoricalFare'] = pd.qcut(train['Fare'], 4)\n# 创建一个新特征 CategoricalAge\nfor dataset in full_data:\n    age_avg = dataset['Age'].mean()\n    age_std = dataset['Age'].std()\n    age_null_count = dataset['Age'].isnull().sum()\n    age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)\n    dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list\n    dataset['Age'] = dataset['Age'].astype(int)\ntrain['CategoricalAge'] = pd.cut(train['Age'], 5)\n\n# 定义函数从 passenger 名字中获取 titles\ndef get_title(name):\n    title_search = re.search(' ([A-Za-z]+)\\.', name)\n    # 如果 title 存在，返回\n    if title_search:\n        return title_search.group(1)\n    return \"\"\n# 创建一个新的特征 Title, 包含乘客的名字的 titles\nfor dataset in full_data:\n    dataset['Title'] = dataset['Name'].apply(get_title)\n# 将所有的非常见的 title 分类到一个 “Rare” 特征\nfor dataset in full_data:\n    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n\n    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n\n# 将数据集中的数据映射成离散型数据\nfor dataset in full_data:\n    # 映射 Sex\n    dataset['Sex'] = dataset['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n    \n    # 映射 titles\n    title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n    dataset['Title'] = dataset['Title'].map(title_mapping)\n    dataset['Title'] = dataset['Title'].fillna(0)\n    \n    # 映射 Embarked\n    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n    \n    # 映射 Fare\n    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] \t\t\t\t\t\t        = 0\n    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n    dataset.loc[ dataset['Fare'] > 31, 'Fare'] \t\t\t\t\t\t\t        = 3\n    dataset['Fare'] = dataset['Fare'].astype(int)\n    \n    # 映射 Age\n    dataset.loc[ dataset['Age'] <= 16, 'Age'] \t\t\t\t\t       = 0\n    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n    dataset.loc[ dataset['Age'] > 64, 'Age'] = 4\n\n# train.head(3)\n\n# 生成一个临时文件，专门存储我们已经转化/映射完成之后的 train 和 test 文件，我写的很烂，，，\ndef saveTmpTrainFile(tmpFile,csvName):\n    with open(csvName, 'w', newline='') as myFile:\n        myWriter=csv.writer(myFile)\n        myWriter.writerow([\"Survived\",\"Pclass\",\"Sex\",\"Age\",\"Parch\",\"Fare\",\"Embarked\",\"Name_length\",\"Has_cabin\",\"FamilySize\",\"IsAlone\",\"Title\"])\n        for lines in tmpFile.index:\n            tmp=[]\n            tmp.append(tmpFile.loc[lines].values[1])\n            tmp.append(tmpFile.loc[lines].values[2])\n            tmp.append(tmpFile.loc[lines].values[4])\n            tmp.append(tmpFile.loc[lines].values[5])\n            tmp.append(tmpFile.loc[lines].values[7])\n            tmp.append(tmpFile.loc[lines].values[9])\n            tmp.append(tmpFile.loc[lines].values[11])\n            tmp.append(tmpFile.loc[lines].values[12])\n            tmp.append(tmpFile.loc[lines].values[13])\n            tmp.append(tmpFile.loc[lines].values[14])\n            tmp.append(tmpFile.loc[lines].values[15])\n            tmp.append(tmpFile.loc[lines].values[-1])\n            myWriter.writerow(tmp)\n            \nsaveTmpTrainFile(train,'D:/titanic/titanic_dataset/train_later.csv')\n\n# 生成一个临时文件，专门存储我们已经转化/映射完成之后的 train 和 test 文件\ndef saveTmpTestFile(tmpFile,csvName):\n    with open(csvName, 'w', newline='') as myFile:\n        myWriter=csv.writer(myFile)\n        myWriter.writerow([\"Pclass\",\"Sex\",\"Age\",\"Parch\",\"Fare\",\"Embarked\",\"Name_length\",\"Has_cabin\",\"FamilySize\",\"IsAlone\",\"Title\"])\n        for lines in tmpFile.index:\n            tmp=[]\n            tmp.append(tmpFile.loc[lines].values[1])\n            tmp.append(tmpFile.loc[lines].values[3])\n            tmp.append(tmpFile.loc[lines].values[4])\n            tmp.append(tmpFile.loc[lines].values[6])\n            tmp.append(tmpFile.loc[lines].values[8])\n            tmp.append(tmpFile.loc[lines].values[10])\n            tmp.append(tmpFile.loc[lines].values[11])\n            tmp.append(tmpFile.loc[lines].values[12])\n            tmp.append(tmpFile.loc[lines].values[13])\n            tmp.append(tmpFile.loc[lines].values[14])\n            tmp.append(tmpFile.loc[lines].values[15])\n            myWriter.writerow(tmp)\n            \n# 存储 test 文件\nsaveTmpFile(test,'D:/titanic/titanic_dataset/test_later.csv')\n\n# # ------------------------------------------------------------------------\n\n# 加载我们生成的 train 和 test 文件\n# # -----------注意一下哈，这个地方，需要调整一下生成的文件-------------------------------------------------------------\ntrain_later = pd.read_csv('D:/titanic/titanic_dataset/train_later.csv')\ntest_later = pd.read_csv('D:/titanic/titanic_dataset/test_later.csv')\n\n# train.head(3)\n# test.head(3)\n\n# # 使用 seaborn 将 特征的 Person 系数画出来\n# colormap = plt.cm.RdBu\n# plt.figure(figsize=(14,12))\n# plt.title('Pearson Correlation of Features', y=1.05, size=15)\n# sns.heatmap(train.astype(float).corr(),linewidths=0.1,vmax=1.0, \n#             square=True, cmap=colormap, linecolor='white', annot=True)\n\n# 一些比较有用参数，后边会派上用场\nntrain = train_later.shape[0]\nntest = test_later.shape[0]\nSEED = 0 # 重现性\nNFOLDS = 5 # 设置交叉验证的折数，以便后面的 K 折交叉验证\nkf = KFold(ntrain, n_folds= NFOLDS, random_state=SEED)\n\n# sklearn 的分类器\nclass SklearnHelper(object):\n    def __init__(self, clf, seed=0, params=None):\n        params['random_state'] = seed\n        self.clf = clf(**params)\n\n    def train(self, x_train, y_train):\n        self.clf.fit(x_train, y_train)\n\n    def predict(self, x):\n        return self.clf.predict(x)\n    \n    def fit(self,x,y):\n        return self.clf.fit(x,y)\n    \n    def feature_importances(self,x,y):\n        print(self.clf.fit(x,y).feature_importances_)\n\n# 获取最后的 预测结果        \ndef get_oof(clf, x_train, y_train, x_test):\n    oof_train = np.zeros((ntrain,))\n    oof_test = np.zeros((ntest,))\n    oof_test_skf = np.empty((NFOLDS, ntest))\n\n    for i, (train_index, test_index) in enumerate(kf):\n        x_tr = x_train[train_index]\n        y_tr = y_train[train_index]\n        x_te = x_train[test_index]\n\n        clf.train(x_tr, y_tr)\n\n        oof_train[test_index] = clf.predict(x_te)\n        oof_test_skf[i, :] = clf.predict(x_test)\n\n    oof_test[:] = oof_test_skf.mean(axis=0)\n    return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)\n\n# 将参数加载到调用的分类器中，进行调试\n# Random Forest 的参数\nrf_params = {\n    'n_jobs': -1,\n    'n_estimators': 500,\n     'warm_start': True, \n     #'max_features': 0.2,\n    'max_depth': 6,\n    'min_samples_leaf': 2,\n    'max_features' : 'sqrt',\n    'verbose': 0\n}\n\n# Extra Trees 的参数\net_params = {\n    'n_jobs': -1,\n    'n_estimators':500,\n    #'max_features': 0.5,\n    'max_depth': 8,\n    'min_samples_leaf': 2,\n    'verbose': 0\n}\n\n# AdaBoost 的参数\nada_params = {\n    'n_estimators': 500,\n    'learning_rate' : 0.75\n}\n\n# Gradient Boosting 的参数\ngb_params = {\n    'n_estimators': 500,\n     #'max_features': 0.2,\n    'max_depth': 5,\n    'min_samples_leaf': 2,\n    'verbose': 0\n}\n\n# Support Vector Classifier 的参数\nsvc_params = {\n    'kernel' : 'linear',\n    'C' : 0.025\n    }\n\n\n# 创建代表模型的 5 个对象\nrf = SklearnHelper(clf=RandomForestClassifier, seed=SEED, params=rf_params)\net = SklearnHelper(clf=ExtraTreesClassifier, seed=SEED, params=et_params)\nada = SklearnHelper(clf=AdaBoostClassifier, seed=SEED, params=ada_params)\ngb = SklearnHelper(clf=GradientBoostingClassifier, seed=SEED, params=gb_params)\nsvc = SklearnHelper(clf=SVC, seed=SEED, params=svc_params)\n\n# 创建 Numpy arrays 来代表 train, test 和 target\ny_train = train['Survived'].ravel()\ntrain = train.drop(['Survived'], axis=1)\nx_train = train.values # 创建 train 的 array\nx_test = test.values # 创建 test 的 array\n\n# 创建您的 OOF 的 train 和 test 预测\net_oof_train, et_oof_test = get_oof(et, x_train, y_train, x_test) # Extra Trees\nrf_oof_train, rf_oof_test = get_oof(rf,x_train, y_train, x_test) # Random Forest\nada_oof_train, ada_oof_test = get_oof(ada, x_train, y_train, x_test) # AdaBoost \ngb_oof_train, gb_oof_test = get_oof(gb,x_train, y_train, x_test) # Gradient Boost\nsvc_oof_train, svc_oof_test = get_oof(svc,x_train, y_train, x_test) # Support Vector Classifier\n\nprint(\"Training is complete\")\n\n# 创建 submission 文件，接下来就是提交到 kaggle 页面看一下得分和排名啦\ndef saveResult(result,csvName):\n    with open(csvName,'w',newline='') as myFile:    \n        myWriter=csv.writer(myFile)\n        myWriter.writerow([\"PassengerId\",\"Survived\"])\n        index=891\n        for i in result:\n            tmp=[]\n            index=index+1\n            tmp.append(index)\n            tmp.append(int(i))\n            myWriter.writerow(tmp)\n            \n\nsaveResult(et_oof_test,'D:/titanic/titanic_dataset/result/et.csv')\nsaveResult(rf_oof_test,'D:/titanic/titanic_dataset/result/rf.csv')\nsaveResult(ada_oof_test,'D:/titanic/titanic_dataset/result/ada.csv')\nsaveResult(gb_oof_test,'D:/titanic/titanic_dataset/result/gb.csv')\nsaveResult(svc_oof_test,'D:/titanic/titanic_dataset/result/svc.csv')"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/titanic/titanic-python3.6.py",
    "content": "#!/usr/bin/python\n# coding: utf-8\n'''\nCreated on 2019-08-14\nUpdate  on 2019-08-31\nAuthor: 片刻\nGithub: https://github.com/apachecn/Interview\n'''\nimport re\nimport datetime\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn import preprocessing\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import KFold, cross_val_score, train_test_split\nfrom xgboost import XGBClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, VotingClassifier\n\n\n# 加载数据\ndef opencsv():\n    root_path = '/opt/data/kaggle/getting-started/titanic'\n\n    tr_data = pd.read_csv('%s/%s' % (root_path, 'input/train.csv'), header=0)\n    te_data = pd.read_csv('%s/%s' % (root_path, 'input/test.csv'), header=0)\n        \n    # print(tr_data.head(5))\n    # print(tr_data.info())\n    # # 返回数值型变量的统计量\n    # print(tr_data.describe())\n\n    # 数据预处理（清洗、缺失值）\n    do_DataPreprocessing(tr_data)\n    # print(tr_data.head(5))\n    # print(tr_data.dtypes)\n    # print(te_data.describe())\n\n    do_DataPreprocessing(te_data)\n    # print(te_data.head(5))\n    # print(te_data.dtypes)\n    # print(te_data.describe())\n\n    # # 相关性分析\n    # # 相关性协方差表, corr()函数,返回结果接近0说明无相关性,大于0说明是正相关,小于0是负相关.\n    # train_corr = tr_data.corr()\n    # print(train_corr)\n    # # 画出相关性热力图\n    # a = plt.subplots(figsize=(15,9))#调整画布大小\n    # a = sns.heatmap(train_corr, vmin=-1, vmax=1 , annot=True , square=True)  #画热力图\n    # plt.show()\n    # \"\"\"\n    #             Survived    Pclass       Sex       Age     Parch      Fare  Embarked     Title  NameLength\n    # Survived    1.000000 -0.338481  0.543351 -0.064910  0.081629  0.257307  0.106811  0.354072    0.332350\n    # Pclass     -0.338481  1.000000 -0.131900 -0.339898  0.018443 -0.549500  0.045702 -0.211552   -0.220001\n    # Sex         0.543351 -0.131900  1.000000 -0.081163  0.245489  0.182333  0.116569  0.419760    0.448759\n    # Age        -0.064910 -0.339898 -0.081163  1.000000 -0.172482  0.096688 -0.009165 -0.037174    0.039702\n    # Parch       0.081629  0.018443  0.245489 -0.172482  1.000000  0.216225 -0.078665  0.235164    0.252282\n    # Fare        0.257307 -0.549500  0.182333  0.096688  0.216225  1.000000  0.062142  0.122872    0.155832\n    # Embarked    0.106811  0.045702  0.116569 -0.009165 -0.078665  0.062142  1.000000  0.055788   -0.107749\n    # Title       0.354072 -0.211552  0.419760 -0.037174  0.235164  0.122872  0.055788  1.000000    0.436099\n    # NameLength  0.332350 -0.220001  0.448759  0.039702  0.252282  0.155832 -0.107749  0.436099    1.000000\n    # \"\"\"\n\n    # 对于PessengerId 忽略，这个是自增长没意义    \n    pids = te_data['PassengerId'].tolist()\n    tr_data.drop(['PassengerId'], axis=1,inplace=True)\n    te_data.drop(['PassengerId'], axis=1,inplace=True)\n    train_data  = tr_data.values[:, 1:]  # 读入全部训练数据,  [行，列]\n    train_label = tr_data.values[:, 0]  # 读取列表的第一列\n    test_data   = te_data.values[:, :]  # 测试全部测试个数据\n    return train_data, train_label, test_data, pids\n\n\ndef do_DataPreprocessing(titanic):\n    \"\"\"\n    | Survival    | 生存                | 0 = No, 1 = Yes |\n    | Pclass      | 票类别-社会地位       | 1 = 1st, 2 = 2nd, 3 = 3rd |  \n    | Name        | 姓名                | |\n    | Sex         | 性别                | |\n    | Age         | 年龄                | |    \n    | SibSp       | 船上的兄弟姐妹/配偶   | | \n    | Parch       | 船上的父母/孩子的数量 | |\n    | Ticket      | 票号                | |   \n    | Fare        | 乘客票价            | |  \n    | Cabin       | 客舱号码            | |    \n    | Embarked    | 登船港口            | C=Cherbourg, Q=Queenstown, S=Southampton |  \n\n    >>> print(titanic.describe())\n           PassengerId    Survived      Pclass         Age       SibSp       Parch        Fare\n    count   891.000000  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n    mean    446.000000    0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n    std     257.353842    0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n    min       1.000000    0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n    25%     223.500000    0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n    50%     446.000000    0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n    75%     668.500000    1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n    max     891.000000    1.000000    3.000000   80.000000    8.000000    6.000000  512.329200\n\n    Pclass  Name                          Sex     Age      SibSp   Parch   Ticket      Fare        Cabin   Embarked\n    3       Braund, Mr. Owen Harris       male    22       1       0       A/5 21171   7.25                S\n    1       Cumings, Mrs. John Bradley    female  38       1       0       PC 17599    71.2833     C85     C\n    \"\"\"\n    # 组合特征(特征组合相关性变差了)\n    # titanic[\"FamilySize\"] = titanic[\"SibSp\"] + titanic[\"Parch\"]\n\n    # 对缺失值处理（Age 中位数不错）\n    titanic[\"Age\"] = titanic[\"Age\"].fillna(titanic[\"Age\"].median())\n    titanic[\"Fare\"] = titanic[\"Fare\"].fillna(titanic[\"Fare\"].median())\n\n    # 对文本特征进行处理（性别， 登船港口）\n    # print(titanic[\"Sex\"].unique())\n    titanic.loc[titanic[\"Sex\"]==\"male\", \"Sex\"] = 0\n    titanic.loc[titanic[\"Sex\"]==\"female\", \"Sex\"] = 1\n\n    # S的概率最大，当然我们也可以按照概率随机算，都可以\n    # print(titanic[\"Embarked\"].unique())\n    \"\"\"\n    titanic[[\"Embarked\"]].groupby(\"Embarked\").agg({\"Embarked\": \"count\"})\n              Embarked\n    Embarked          \n    C              168\n    Q               77\n    S              644\n    \"\"\"\n    titanic[\"Embarked\"] = titanic[\"Embarked\"].fillna('S')\n    titanic.loc[titanic[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n    titanic.loc[titanic[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n    titanic.loc[titanic[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n\n    def get_title(name):\n        # 名字的尊称\n        title_search = re.search(' ([A-Za-z]+)\\.', name)\n        if title_search:\n            return title_search.group(1)\n        return \"\"\n    titles = titanic[\"Name\"].apply(get_title)\n    # print(pd.value_counts(titles))\n    # 对尊称建立mapping字典\n    title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Dr\": 5, \"Rev\": 6, \"Major\": 7, \"Col\": 7, \"Mlle\": 8, \"Mme\": 8, \"Don\": 9, \"Dona\": 9, \"Lady\": 10, \"Countess\": 10, \"Jonkheer\": 10, \"Sir\": 9, \"Capt\": 7, \"Ms\": 2}\n    for k, v in title_mapping.items():\n        titles[titles == k] = v\n    # print(pd.value_counts(titles))\n\n    # 添加一个新特征表示拥护尊称\n    titanic[\"Title\"] = [int(i) for i in titles.values.tolist()]\n    # 添加一个新特征表示名字长度\n    titanic[\"NameLength\"] = titanic[\"Name\"].apply(lambda x: len(x))\n\n    # 相关性太差，删除\n    # titanic.drop(['PassengerId'], axis=1,inplace=True)\n    titanic.drop(['Cabin'], axis=1,inplace=True)\n    titanic.drop(['SibSp'], axis=1,inplace=True)\n    # titanic.drop(['Parch'],axis=1,inplace=True)\n    titanic.drop(['Ticket'], axis=1,inplace=True)\n    titanic.drop(['Name'],   axis=1,inplace=True)\n\n\ndef do_FeatureEngineering(data, COMPONENT_NUM=0.9):\n    # scale values  归一化\n    scaler = preprocessing.StandardScaler()\n    s_data = scaler.fit_transform(data)\n    return s_data\n\n    # # 降维(不降维，准确率还上升了)\n    # '''\n    # 使用说明：https://www.cnblogs.com/pinard/p/6243025.html\n    # n_components>=1\n    #   n_components=NUM   设置占特征数量比\n    # 0 < n_components < 1\n    #   n_components=0.99  设置阈值总方差占比\n    # '''\n    # pca = PCA(n_components=COMPONENT_NUM, whiten=False)\n    # # 只训练 训练集数据\n    # pca.fit(s_data)  # Fit the model with X\n    # # 训练集 和 测试集 保持一直，进行 transform\n    # pca_data = pca.transform(s_data)  # Fit the model with X and 在X上完成降维.\n\n    # # pca 方差大小、方差占比、特征数量\n    # # print(\"方差大小:\\n\", pca.explained_variance_, \"方差占比:\\n\", pca.explained_variance_ratio_)\n    # print(\"特征数量: %s\" % pca.n_components_)\n    # print(\"总方差占比: %s\" % sum(pca.explained_variance_ratio_))\n\n    # return pca_data\n\n\ndef trainModel(trainData, trainLabel):\n\n    # 模拟测试\n    # 0.881994680700563 [0.87480519 0.91168831 0.89090909 0.85294118 0.87962963]\n    # model = LogisticRegression(random_state=1)\n    # 0.8819594261947202 [0.87532468 0.91272727 0.89298701 0.84912854 0.87962963]\n    # model = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2)\n    # 0.8812371898254252 [0.87428571 0.90857143 0.89402597 0.84803922 0.88126362]\n    # model = RandomForestClassifier(random_state=1, n_estimators=50, min_samples_split=8, min_samples_leaf=4)\n\n    # # 网格搜索 #####\n    # from sklearn.model_selection import GridSearchCV\n    # param_test = {\n    #     # 'n_estimators': np.arange(190, 240, 2), \n    #     'max_depth': np.arange(4, 7, 1), \n    #     'learning_rate': np.array([0.01, 0.03, 0.05, 0.08, 0.1, 0.15, 0.2]), \n\n    #     'n_estimators': np.array([222]), \n    #     # 'max_depth': np.array([4]),     \n    #     # 'learning_rate': np.array([0.01, 0.02, 0.03, 0.04, 0.05]), \n    #     # 'min_child_weight': np.arange(1, 6, 2), \n    #     # 'C': (1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9)\n    # }\n\n    # 0.8294499549079417   [0.82122905 0.80446927 0.86516854 0.82022472 0.83615819]\n    # model = XGBClassifier()\n    # grid_search = GridSearchCV(estimator=model, param_grid=param_test, scoring='roc_auc', cv=5)\n    # grid_search.fit(trainData, trainLabel)\n    # print(\"最优得分 >>>\", grid_search.best_score_)\n    # print(\"最优参数 >>>\", grid_search.best_params_)\n\n    # model = XGBClassifier(learning_rate = 0.1,  n_estimators= 202, max_depth= 4,\n    #             min_child_weight= 5, gamma=0, subsample=0.8, colsample_bytree=0.8, \n    #             objective= 'binary:logistic', scale_pos_weight=1\n    # )\n    # 0.8825292702939761 [0.87272727 0.91376623 0.89194805 0.84912854 0.88507625]\n    # 0.8812472625413802 [0.87480519 0.91012987 0.89090909 0.85239651 0.87799564]\n    # model = XGBClassifier(n_estimators=222, max_depth=4, learning_rate=0.03)\n\n    # scores = cross_val_score(model, trainData, trainLabel, cv=5, scoring='roc_auc')\n    # print(scores.mean(), \"\\n\", scores)\n    print(\"模型融合\")\n    \"\"\"\n    Bagging:   同一模型的投票选举\n    Boosting:  同一模型的再学习\n    Voting:    不同模型的投票选举\n    Stacking:  分层预测 – K-1份数据预测1份模型拼接，得到 预测结果*算法数（作为特征） => 从而预测最终结果\n    Blending:  分层预测 – 将数据分成2部分（A部分训练B部分得到预测结果），得到 预测结果*算法数（作为特征） => 从而预测最终结果\n    \"\"\"\n    # 1. Bagging 算法实现\n    # 0.8818756755227344 [0.87324675 0.91428571 0.89090909 0.85076253 0.88017429]\n    # model = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2)\n\n    # 2. Boosting 算法实现\n    # 0.8488710896477386  [0.8198946  0.82285903 0.87780749 0.84906417 0.87473017]\n    # model = AdaBoostClassifier(random_state=1, n_estimators=100, learning_rate=1)\n\n    # 3. Voting\n    # 0.8815388054211584 [0.87480519 0.91272727 0.89194805 0.84912854 0.87908497]\n    # model = VotingClassifier(\n    #     estimators=[\n    #         ('log_clf', LogisticRegression()),\n    #         ('ab_clf', AdaBoostClassifier()),\n    #         ('svm_clf', SVC(probability=True)),\n    #         ('rf_clf', RandomForestClassifier()),\n    #         ('gbdt_clf', GradientBoostingClassifier()),\n    #         ('rb_clf', AdaBoostClassifier())\n    #     ], voting='soft') # , voting='hard')\n    # scores = cross_val_score(model, trainData, trainLabel, cv=5, scoring='roc_auc')\n    # print(scores.mean(), \"\\n\", scores)\n    \n    # # 4. Stacking\n    # # 0.8813662677192088 [0.87532468 0.90805195 0.89142857 0.85348584 0.87854031]\n    # clfs = [\n    #     AdaBoostClassifier(),\n    #     SVC(probability=True),\n    #     AdaBoostClassifier(),\n    #     LogisticRegression(C=0.1,max_iter=100),\n    #     XGBClassifier(max_depth=6,n_estimators=100,num_round = 5),\n    #     RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True),\n    #     GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)\n    # ]\n    # kf = KFold(n_splits=5, shuffle=True, random_state=1)\n\n    # # 创建零矩阵\n    # dataset_stacking_train = np.zeros((trainData.shape[0], len(clfs)))\n    # # dataset_stacking_label  = np.zeros((trainLabel.shape[0], len(clfs)))\n\n    # for j, clf in enumerate(clfs):\n    #     '''依次训练各个单模型'''\n    #     for i,(train, test) in enumerate(kf.split(trainLabel)):\n    #         '''使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。'''\n    #         # print(\"Fold\", i)\n    #         X_train, y_train, X_test, y_test = trainData[train], trainLabel[train], trainData[test], trainLabel[test]\n    #         clf.fit(X_train, y_train)\n    #         y_submission = clf.predict_proba(X_test)[:, 1]\n\n    #         # j 表示每一次的算法，而 test是交叉验证得到的每一行（也就是每一个算法把测试机和都预测了一遍）\n    #         dataset_stacking_train[test, j] = y_submission\n\n    # # 用建立第二层模型\n    # model = LogisticRegression(C=0.1, max_iter=100)\n    # model.fit(dataset_stacking_train, trainLabel)\n    # scores = cross_val_score(model, dataset_stacking_train, trainLabel, cv=5, scoring='roc_auc')\n    # print(scores.mean(), \"\\n\", scores)\n    \n    # 5. Blending\n    # 0.8838950287185581 [0.87584416 0.91064935 0.89714286 0.85294118 0.8828976 ]\n    clfs = [\n        AdaBoostClassifier(),\n        SVC(probability=True),\n        AdaBoostClassifier(),\n        LogisticRegression(C=0.1,max_iter=100),\n        XGBClassifier(max_depth=6,n_estimators=100,num_round = 5),\n        RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True),\n        GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)\n    ]\n    X_d1, X_d2, y_d1, y_d2 = train_test_split(trainData, trainLabel, test_size=0.5, random_state=2017)\n    dataset_d1 = np.zeros((X_d2.shape[0], len(clfs)))\n    dataset_d2 = np.zeros((trainLabel.shape[0], len(clfs)))\n\n    for j, clf in enumerate(clfs):\n        #依次训练各个单模型\n        # 对于测试集，直接用这k个模型的预测值作为新的特征。\n        clf.fit(X_d1, y_d1)\n        dataset_d1[:, j] = clf.predict_proba(X_d2)[:, 1]\n    # 用建立第二层模型\n    model = LogisticRegression(C=0.1, max_iter=100)\n    model.fit(dataset_d1, y_d2)\n\n    scores = cross_val_score(model, dataset_d1, y_d2, cv=5, scoring='roc_auc')\n    print(scores.mean(), \"\\n\", scores)\n    return model\n\n\ndef main():\n    # 开始时间\n    sta_time = datetime.datetime.now()\n\n    # 1.加载数据和预处理\n    train_data, train_label, test_data, pids = opencsv()\n\n    # 2. 特征工程\n    pca_tr_data = do_FeatureEngineering(train_data)\n    pca_te_data = do_FeatureEngineering(test_data)\n\n    # 3. 模型训练/模型融合（分类问题： lr、rf、adboost、xgboost、lightgbm）\n    model = trainModel(pca_tr_data, train_label)\n    model.fit(pca_tr_data, train_label)\n    labels = model.predict(pca_te_data)\n\n    # 4. 数据导出\n    print(type(pids), type(labels.tolist()))\n    result = pd.DataFrame({\n        'PassengerId': pids, \n        'Survived': [int(i) for i in labels.tolist()]\n    })\n    result.to_csv('Result_titanic.csv', index=False)\n\n    # 结束时间\n    end_time = datetime.datetime.now()\n    times = (end_time - sta_time).seconds\n    print(\"\\n运行时间: %ss == %sm == %sh\\n\\n\" % (times, times/60, times/60/60))\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/titanic/titanic.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 2017-12-07\nUpdate  on 2017-12-24\nTeam:   一把梭\nGithub: https://github.com/apachecn/kaggle\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\nfrom sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier\nfrom sklearn.ensemble import VotingClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import cross_val_score\n\n# 0. 数据读入及预处理\nroot_path = 'datasets/getting-started/titanic/input'\ndata_train = pd.read_csv('%s/%s' % (root_path, 'train.csv'))\n# data_train.info()\n# print(data_train.describe())\n\n# 1. 去除唯一属性特\ndata_train.drop(['PassengerId', 'Ticket'], axis=1, inplace=True)\n\n# 2. 类别特征One-Hot编码\ndata_train['Sex'] = data_train['Sex'].map({'female': 0, 'male': 1}).astype(np.int64)\ndata_train.loc[data_train.Embarked.isnull(), 'Embarked'] = 'S' # 2个Embarked缺失值直接填充为S\ndata_train = pd.concat([data_train, pd.get_dummies(data_train.Embarked)], axis=1)\ndata_train = data_train.rename(columns={'C': 'Cherbourg','Q': 'Queenstown','S': 'Southampton'})\n\n# 将名字转换\ndef replace_name(x):\n    if 'Mrs' in x: return 'Mrs'\n    elif 'Mr' in x: return 'Mr'\n    elif 'Miss' in x: return 'Miss'\n    else: return 'Other'\n\ndata_train['Name'] = data_train['Name'].map(lambda x:replace_name(x))\ndata_train = pd.concat([data_train, pd.get_dummies(data_train.Name)], axis=1)\ndata_train = data_train.rename(columns={'Miss': 'Name_Miss','Mr': 'Name_Mr',\n                                        'Mrs': 'Name_Mrs','Other': 'Name_Other'})\n\n# 3. 数值特征标准化\ndef fun_scale(df_feature):\n    np_feature = df_feature.values.reshape(-1,1).astype(np.float64)\n    feature_scale = StandardScaler().fit(np_feature)\n    feature_scaled = StandardScaler().fit_transform(np_feature, feature_scale)\n    return feature_scale, feature_scaled\n\nPclass_scale, data_train['Pclass_scaled'] = fun_scale(data_train['Pclass'])\nFare_scale, data_train['Fare_scaled'] = fun_scale(data_train['Fare'])\nSibSp_scale, data_train['SibSp_scaled'] = fun_scale(data_train['SibSp'])\nParch_scale, data_train['Parch_scaled'] = fun_scale(data_train['Parch'])\n\n# 4. 缺失值补全及相应处理\n# 处理Age缺失值并标准化\n# 缺失值处理函数\ndef set_missing_feature(train_for_missingkey, data, info):\n    known_feature = train_for_missingkey[train_for_missingkey.Age.notnull()].as_matrix()\n    unknown_feature = train_for_missingkey[train_for_missingkey.Age.isnull()].as_matrix()\n    y = known_feature[:, 0] # 第1列作为待补全属性\n    x = known_feature[:, 1:] # 第2列及之后的属性作为预测属性\n    rf = RandomForestRegressor(random_state=0, n_estimators=100)\n    rf.fit(x, y)\n    print(info, \"缺失值预测得分\", rf.score(x, y))\n    predictage = rf.predict(unknown_feature[:, 1:])\n    data.loc[data.Age.isnull(), 'Age'] = predictage\n    return data\n\ntrain_for_missingkey_train = data_train[['Age','Survived','Sex','Name_Miss','Name_Mr','Name_Mrs',\n                                         'Name_Other','Fare_scaled','SibSp_scaled','Parch_scaled']]\ndata_train = set_missing_feature(train_for_missingkey_train, data_train,'Train_Age')\nAge_scale, data_train['Age_scaled'] = fun_scale(data_train['Age'])\n\n# 处理Cabin特征\ndef set_Cabin_type(df):\n    df.loc[ (df.Cabin.notnull()), 'Cabin' ] = 1.\n    df.loc[ (df.Cabin.isnull()), 'Cabin' ] = 0.\n    return df\n\ndata_train = set_Cabin_type(data_train)\n\n# 5. 整合数据\ntrain_X = data_train[['Sex','Cabin','Cherbourg','Queenstown','Southampton','Name_Miss','Name_Mr','Name_Mrs','Name_Other',\n                      'Pclass_scaled','Fare_scaled','SibSp_scaled','Parch_scaled','Age_scaled']].as_matrix()\ntrain_y = data_train['Survived'].as_matrix()\n\n# 6. 模型搭建及交叉验证\nlr = LogisticRegression(C=1.0, tol=1e-6)\nsvc = SVC(C=1.1, kernel='rbf', decision_function_shape='ovo')\nadaboost = AdaBoostClassifier(n_estimators=490, random_state=0)\nrandomf = RandomForestClassifier(n_estimators=185, max_depth=5, random_state=0)\ngbdt = GradientBoostingClassifier(n_estimators=436, max_depth=2, random_state=0)\nVotingC = VotingClassifier(estimators=[('LR',lr),('SVC',svc),('AdaBoost',adaboost),\n                                      ('RandomF',randomf),('GBDT',gbdt)])\n\n'''\n# 交叉验证部分 #####\nparam_test = {\n        'n_estimators': np.arange(200, 240, 1), \n        'max_depth': np.arange(4, 7, 1), \n        #'min_child_weight': np.arange(1, 6, 2), \n        #'C': (1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9)\n        }\n\nfrom sklearn.grid_search import GridSearchCV\n\ngrid_search = GridSearchCV(estimator=xgbClassifier, param_grid=param_test, scoring='roc_auc', cv=5)\ngrid_search.fit(train_X,train_y)\ngrid_search.grid_scores_, grid_search.best_params_, grid_search.best_score_\n# 交叉验证部分 #####\n'''\n\n# 模型训练及交叉验证\nclassifierlist = [('LR',lr),('SVC',svc),('AdaBoost',adaboost),('RandomF',randomf),\n                  ('GBDT',gbdt),('VotingC',VotingC)]\nfor name, classifier in classifierlist:\n    # 分类器训练与下一步交叉验证无关，训练是为下面测试集预测使用\n    classifier.fit(train_X, train_y) \n    print(name, \"Mean_Cross_Val_Score is:\", \n          cross_val_score(classifier, train_X, train_y, cv=5, scoring='accuracy').mean(), \"\\n\")\n\n# 7. 测试集处理\ndata_test = pd.read_csv('test.csv')\ndata_test.drop(['Ticket'], axis=1, inplace=True)\n\ndata_test['Sex'] = data_test['Sex'].map({'female': 0, 'male': 1}).astype(np.int64)\ndata_test = pd.concat([data_test, pd.get_dummies(data_test.Embarked)], axis=1)\ndata_test = data_test.rename(columns={'C': 'Cherbourg','Q': 'Queenstown','S': 'Southampton'})\n\ndata_test['Name'] = data_test['Name'].map(lambda x:replace_name(x))\ndata_test = pd.concat([data_test, pd.get_dummies(data_test.Name)], axis=1)\ndata_test = data_test.rename(columns={'Miss': 'Name_Miss','Mr': 'Name_Mr',\n                                      'Mrs': 'Name_Mrs','Other': 'Name_Other'})\n\n# 测试集标准化函数\ndef fun_test_scale(feature_scale, df_feature):\n    np_feature = df_feature.values.reshape(-1,1).astype(np.float64)\n    feature_scaled = StandardScaler().fit_transform(np_feature, feature_scale)\n    return feature_scaled\n\ndata_test['Pclass_scaled'] = fun_test_scale(Pclass_scale, data_test['Pclass'])\ndata_test.loc[data_test.Fare.isnull(),'Fare'] = 0 # 缺失值置为0\ndata_test['Fare_scaled'] = fun_test_scale(Fare_scale, data_test['Fare'])\ndata_test['SibSp_scaled'] = fun_test_scale(SibSp_scale, data_test['SibSp'])\ndata_test['Parch_scaled'] = fun_test_scale(Parch_scale, data_test['Parch'])\n\n# 处理测试集Age缺失值并归一化\ntrain_for_missingkey_test = data_test[['Age','Sex','Name_Miss','Name_Mr','Name_Mrs','Name_Other',\n                                       'Fare_scaled','SibSp_scaled','Parch_scaled']]\ndata_test = set_missing_feature(train_for_missingkey_test, data_test, 'Test_Age')\ndata_test['Age_scaled'] = fun_test_scale(Age_scale, data_test['Age'])\n\ndata_test = set_Cabin_type(data_test)\n\ntest_X = data_test[['Sex','Cabin','Cherbourg','Queenstown','Southampton','Name_Miss','Name_Mr','Name_Mrs','Name_Other',\n                    'Pclass_scaled','Fare_scaled','SibSp_scaled','Parch_scaled','Age_scaled']].as_matrix()\n\n# 8. 模型预测\nmodel = classifierlist[4] # 选择分类器\nprint(\"Test in %s!\" % model[0])\npredictions = model[1].predict(test_X).astype(np.int32)\nresult = pd.DataFrame({'PassengerId':data_test['PassengerId'].as_matrix(), 'Survived':predictions})\nresult.to_csv('Result_with_%s.csv' % model[0], index=False)\nprint('...\\nAll Finish!')\n\n\n# 9. XGBoost\nimport xgboost as xgb\n\nfrom sklearn.model_selection import train_test_split\nx_train, x_valid, y_train, y_valid = train_test_split(train_X, train_y, test_size=0.1, random_state=0)\nprint(x_train.shape, x_valid.shape)\n\nxgbClassifier = xgb.XGBClassifier(learning_rate = 0.1, \n                                  n_estimators= 234,\n                                  max_depth= 6,\n                                  min_child_weight= 5,\n                                  gamma=0, \n                                  subsample=0.8, \n                                  colsample_bytree=0.8, \n                                  objective= 'binary:logistic', \n                                  scale_pos_weight=1)\n\nxgbClassifier.fit(train_X, train_y)\nxgbpred_test = xgbClassifier.predict(test_X).astype(np.int32)\nresult = pd.DataFrame({'PassengerId':data_test['PassengerId'].as_matrix(), 'Survived':xgbpred_test})\nresult.to_csv('Result_with_%s.csv' % 'XGBoost', index=False)\n"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/word2vec-nlp-tutorial/delete-tmp.py",
    "content": "# coding: utf-8\n\nfrom sklearn import svm, datasets\nfrom sklearn.model_selection import GridSearchCV\n\niris = datasets.load_iris()\nparameters = {\"kernel\": (\"linear\", \"rbf\"), \"C\": range(1, 10)}\nsvr = svm.SVC()\n\n'''\nclf.fit(): 运行网格搜索\ngrid_scores_: 给出不同参数情况下的评价结果\nbest_params_: 描述了已取得最佳结果的参数的组合\nbest_score_: 成员提供优化过程期间观察到的最好的评分\n'''\nclf = GridSearchCV(svr, parameters)\nclf.fit(iris.data, iris.target)\nprint(clf.grid_scores_)  # 所有情况的评价结果\nprint(clf.best_params_)  # 最好的参数\nprint(clf.best_score_)   # 最好的参数的平均得分\n"
  },
  {
    "path": "src/py3.x/kaggle/getting-started/word2vec-nlp-tutorial/test.py",
    "content": "#!/usr/bin/python\n# coding: utf-8\n\n'''\nCreated on 2017-12-26\nUpdate  on 2017-12-26\nAuthor: xiaomingnio\nGithub: https://github.com/apachecn/kaggle\nProject: https://www.kaggle.com/c/word2vec-nlp-tutorial\n'''\nimport pandas as pd\nimport numpy as np\nimport re\nfrom bs4 import BeautifulSoup\n\n\ndef review_to_wordlist(review):\n    '''\n    把IMDB的评论转成词序列\n    参考：http://blog.csdn.net/longxinchen_ml/article/details/50629613\n    '''\n    # 去掉HTML标签，拿到内容\n    review_text = BeautifulSoup(review, \"html.parser\").get_text()\n    # 用正则表达式取出符合规范的部分\n    review_text = re.sub(\"[^a-zA-Z]\", \" \", review_text)\n    # 小写化所有的词，并转成词list\n    words = review_text.lower().split()\n    # 返回words\n    return words\n\n\nroot_dir = \"/opt/data/kaggle/getting-started/word2vec-nlp-tutorial\"\n# 载入数据集\ntrain = pd.read_csv('%s/%s' % (root_dir, 'labeledTrainData.tsv'), header=0, delimiter=\"\\t\", quoting=3)\ntest = pd.read_csv('%s/%s' % (root_dir, 'testData.tsv'), header=0, delimiter=\"\\t\", quoting=3)\nprint(train.head())\nprint(test.head())\n"
  },
  {
    "path": "src/py3.x/kaggle/playground/dogs-vs-cats/Pytorch-CNN.py",
    "content": "# -*- coding: utf-8 -*-\n\n'''\n PyTorch 版本的 CNN 实现 Dogs vs Cats\n'''\n\n# 引入相应的库函数\nimport os\nfrom PIL import Image\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.optim\nfrom torch.autograd import Variable\nfrom torch.utils import data\nfrom torchvision import transforms as T\n\n# 我们暂时使用 AlexNet 模型做第一次测试\nfrom models import AlexNet\n\n'''\n参考链接：\nhttps://github.com/pytorch/examples/blob/27e2a46c1d1505324032b1d94fc6ce24d5b67e97/imagenet/main.py\n'''\n\n'''\n这里我们使用了 torch.utils.data 中的一些函数，比如 Dataset\nclass torch.utils.data.Dataset\n表示 Dataset 的抽象类\n所有其他的数据集都应继承该类。所有子类都应该重写 __len__ ，提供数据集大小的方法，和 __getitem__ ，支持从 0 到 len(self) 整数索引的方法\n'''\n# --------------------------- 1.加载数据 start ----------------------------------------------------------------\nclass GetData(data.Dataset):\n    def __init__(self, root, transforms=None, train=True, test=Flase):\n        '''\n        Desc:\n            获取全部的数据，并根据我们的要求，将数据划分为 训练、验证、测试数据集\n        Args:\n            self --- none\n            root --- 数据存在路径\n            transforms --- 对数据的转化，这里默认是 None\n            train ---- 标注是否是训练集\n            test  ---- 标注是否是测试集\n        Returns:\n            None\n        '''\n        # 设置 测试集数据\n        self.test = test\n        imgs = [os.path.join(root, img) for img in os.listdir(root)]\n\n        # test1：即测试数据集， D:/dataset/dogs-vs-cats/test1\n        # train: 即训练数据集，D:/dataset/dogs-vs-cats/train\n        if self.test:\n            # 提取 测试数据集的序号，\n            # 如 x = 'd:/path/123.jpg'，\n            # x.split('.') 得到 ['d:/path/123', 'jpg'] \n            # x.split('.')[-2] 得到 d:/path/123\n            # x.split('.')[-2].split('/') 得到 ['d:', 'path', '123']\n            # x.split('.')[-2].split('/')[-1] 得到 123\n            imgs = sorted(imgs, key=lambda x: int(x.split('.')[-2].split('/')[-1]))\n        else:\n            # 如果不是测试集的话，就是训练集，我们只切分一下，仍然得到序号，123\n            imgs = sorted(imgs, key=lambda x: int(x.split('.')[-2]))\n        \n        # 获取图片的数量\n        imgs_num = len(imgs)\n\n        # 划分训练、验证集，验证集:训练集 = 3:7\n        # 判断是否为测试集\n        if self.test:\n            # 如果是 测试集，那么 就直接赋值\n            self.imgs = imgs\n        # 判断是否为 训练集\n        elif train:\n            # 如果是训练集，那么就把数据集的开始位置的数据 到 70% 部分的数据作为训练集\n            self.imgs = imgs[:int(0.7 * imgs_num)]\n        else:\n            # 这种情况就是划分验证集啦,从 70% 部分的数据 到达数据的末尾，全部作为验证集\n            self.imgs = imgs[int(0.7 * imgs_num):]\n\n        # 数据的转换操作，测试集，验证集，和训练集的数据转换有所区别\n        if transforms is None:\n            # 如果转换操作没有设置，那我们设置一个转换 \n            '''\n            几个常见的 transforms 用的转换：\n            1、数据归一化 --- Normalize(mean, std) 是通过下面公式实现数据归一化 channel = (channel-mean)/std\n            2、class torchvision.transforms.Resize(size, interpolation=2) 将输入的 PIL 图像调整为给定的大小\n            3、class torchvision.transforms.CenterCrop(size) 在中心裁剪给定的 PIL 图像，参数 size 是期望的输出大小\n            4、ToTensor() 是将 PIL.Image(RGB) 或者 numpy.ndarray(H X W X C) 从 0 到 255 的值映射到 0~1 的范围内，并转化为 Tensor 形式\n            5、transforms.Compose() 这个是将多个 transforms 组合起来使用\n            '''\n            normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n            # 测试集 和 验证集 的转换\n            # 判断如果是测试集或者不是训练集（也就是说是验证集），就应用我们下边的转换\n            if self.test or not train:\n                self.trainsforms = T.Compose([T.Resize(224), T.CenterCrop(224), T.ToTensor(), normalize])\n            else:\n                # 如果是测试集的话，使用另外的转换\n                self.transforms = T.Compose([T.Resize(256), T.RandomResizedCrop(224), T.RandomHorizontalFlip(), T.ToTensor(), normalize])\n\n    def __len__(self):\n        '''\n        Desc:\n            继承 Dataset 基类，重写 __len__ 方法，提供数据集的大小\n        Args:\n            self --- 无\n        Return:\n            数据集的长度\n        '''\n        return len(self.imgs)\n\n    def __getitem__(self, index):\n        '''\n        Desc:\n            继承 Dataset 基类，重写 __getitem__ 方法，支持整数索引，范围从 0 到 len(self) \n            返回一张图片的数据\n            对于测试集，没有label，返回图片 id，如 985.jpg 返回 985\n            对于训练集，是具有 label ，返回图片 id ，以及相对应的 label，如 dog.211.jpg 返回 id 为 211，label 为 dog\n        Args:\n            self --- none\n            index --- 索引\n        Return:\n            返回一张图片的数据\n            对于测试集，没有label，返回图片 id，如 985.jpg 返回 985\n            对于训练集，是具有 label ，返回图片 id ，以及相对应的 label，如 dog.211.jpg 返回 id 为 211，label 为 dog\n        '''\n        img_path = self.imgs[index]\n        # 判断，如果是测试集的数据的话，那就返回对应的序号，比如 d:path/123.jpg 返回 123\n        if self.test:\n            label = int(self.imgs[index].split('.')[-2].split('/')[-1])\n        else:\n            # 如果不是测试集的数据，那么会有相应的类别（label），也就是对应的dog 和 cat，dog 为 1，cat 为0\n            label = 1 if 'dog' in img_path.split('/')[-1] else 0\n        # 这里使用 Pillow 模块，使用 Image 打开一个图片\n        data = Image.open(img_path)\n        # 使用我们定义的 transforms ，将图片转换，详情参考：https://pytorch.org/docs/stable/torchvision/transforms.html#transforms-on-pil-image\n        # 默认的 transforms 设置的是 none\n        data = self.transforms(data)\n        # 将转换完成的 data 以及对应的 label（如果有的话），返回\n        return data,label\n    \n# 训练数据集的路径\ntrain_path = 'D:/dataset/dogs-vs-cats/train'\n# 从训练数据集的存储路径中提取训练数据集\ntrain_dataset = GetData(train_path, train=True)\n# 将训练数据转换成 mini-batch 形式\nload_train = data.DataLoader(train_dataset, batch_size=20, shuffle=True, num_workers=1)\n\n'''\nutils.data.DataLoader() 解析\nclass torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=<function default_collate>, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None)\n数据加载器。组合数据集和采样器，并在数据集上提供单个或多个进程迭代器。\n参数：\ndataset(Dataset) --- 从这之中加载数据的数据集。\nbatch_size (int, 可选) --- 每个 batch 加载多少个样本（默认值为：1）\nshuffle (bool, 可选) --- 设置为 True 时，会在每个 epoch 时期重新组合数据（默认值：False）\nsampler (Sampler, 可选) --- 定义从数据集中抽取样本的策略。如果指定，那么 shuffle 必须是 False 。\nbatch_sampler (Sampler, 可选) --- 类似采样器，但一次返回一批量的索引（index）。与 batch_size, shuffle, sampler 和 drop_last 相互排斥。\nnum_workers (int, 可选) --- 设置有多少个子进程用于数据加载。0 表示数据将在主进程中加载。（默认：0）\ncollate_fn (callable, 可选) --- 合并样本列表以形成 mini-batch \npin_memory (bool, 可选) ---  如果为 True，数据加载器会在 tensors 返回之前将 tensors 复制到 CUDA 固定内存中。\ndrop_last (bool, 可选) --- 如果 dataset size （数据集大小）不能被 batch size （批量大小）整除，则设置为 True 以删除最后一个 incomplete batch（未完成批次）。\n                          如果设置为 False 和 dataset size（数据集大小）不能被 batch size（批量大小）整除，则最后一批将会更小。（默认：False）\ntimeout (numeric, 可选) --- 如果是正值，则为从 worker 收集 batch 的超时值。应该始终是非负的。（默认：0）\nworker_init_fn (callable, 可选) --- 如果不是 None，那么将在每个工人子进程上使用 worker id（在 [0，num_workers - 1] 中的 int）作为输入，在 seeding 和加载数据之前调用这个子进程。（默认：无）\n'''\n\n# 测试数据的获取\n# 首先设置测试数据的路径\ntest_path = 'D:/dataset/dogs-vs-cats/test1'\n# 从测试数据集的存储路径中提取测试数据集\ntest_path = GetData(test_path, test=True)\n# 将测试数据转换成 mini-batch 形式\nloader_test = data.DataLoader(test_dataset, batch_size=3, shuffle=True, num_workers=1)\n\n# --------------------------- 1.加载数据 end ----------------------------------------------------------------\n\n# --------------------------- 2.构建 CNN 模型 start ----------------------------------------------------------------\n# 调用我们现成的 AlexNet() 模型\ncnn = AlexNet()\n# 将模型打印出来观察一下\nprint(cnn)\n\n# --------------------------- 2.构建 CNN 模型 end ------------------------------------------------------------------\n\n# --------------------------- 3.设置相应的优化器和损失函数 start ------------------------------------------------------------------\n\n'''\n1、torch.optim 是一个实现各种优化算法的软件包。\n比如我们这里使用的就是 Adam() 这个方法\nclass torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False) 这个类就实现了 adam 算法。\nparams(iterable) --- 可迭代的参数来优化或取消定义参数组\nlr(float, 可选) --- 学习率（默认值 1e-3）\nbeta(Tuple[float, float], 可选) --- 用于计算梯度及其平方的运行平均值的系数（默认值：（0.9，0.999））\neps (float, 可选) ---- 添加到分母以提高数值稳定性（默认值：1e-8）\nweight_decay (float, 可选) --- 权重衰减（L2 惩罚）（默认值：0）\namsgrad (boolean, 可选) ---- 是否使用该算法的AMSGrad变体来自论文关于 Adam 和 Beyond 的融合  \n\n\n2、还有这里我们使用的损失函数 \nclass torch.nn.CrossEntropyLoss(weight=None, size_average=True, ignore_index=-100, reduce=True)\n交叉熵损失函数\n具体的请看：http://pytorch.apachecn.org/cn/docs/0.3.0/nn.html   \n'''\n# 3. 设置优化器和损失函数\n# 这里我们使用 Adam 优化器，使用的损失函数是 交叉熵损失\noptimizer = torch.optim.Adam(cnn.parameters(), lr=0.005, betas=(0.9, 0.99))  # 优化所有的 cnn 参数\nloss_func = nn.CrossEntropyLoss()  # 目标 label 不是 one-hotted 类型的\n\n# --------------------------- 3.设置相应的优化器和损失函数 end ------------------------------------------------------------------\n\n# --------------------------- 4.训练 CNN 模型 start ------------------------------------------------------------------\n\n# 4. 训练模型\n# 设置训练模型的次数，这里我们设置的是 10 次，也就是用我们的训练数据集对我们的模型训练 10 次，为了节省时间，我们可以只训练 1 次\nEPOCH = 10\n# 训练和测试\nfor epoch in range(EPOCH):\n        num = 0\n        # 给出 batch 数据，在迭代 train_loader 的时候对 x 进行 normalize\n        for step, (x, y) in enumerate(loader_train):\n            b_x = Variable(x)  # batch x\n            b_y = Variable(y)  # batch y\n\n            output = cnn(b_x)  # cnn 的输出\n            loss = loss_func(output, b_y)  # 交叉熵损失\n            optimizer.zero_grad()  # 在这一步的训练步骤上，进行梯度清零\n            loss.backward()  # 反向传播，并进行计算梯度\n            optimizer.step()  # 应用梯度\n\n            # 可以打印一下\n            # print('-'*30, step)\n            if step % 20 == 0:\n                num += 1\n                for _, (x_t, y_test) in enumerate(loader_test):\n                    x_test = Variable(x_t)  # batch x\n                    test_output = cnn(x_test)\n                    pred_y = torch.max(test_output, 1)[1].data.squeeze()\n                    accuracy = sum(pred_y == y_test) / float(y_test.size(0))\n                    print('Epoch: ', epoch, '| Num: ',  num, '| Step: ',  step, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.4f' % accuracy)\n\n# --------------------------- 4. 训练 CNN 模型 end ------------------------------------------------------------------"
  },
  {
    "path": "src/py3.x/kaggle/playground/dogs-vs-cats/main的副本.py",
    "content": "import argparse\nimport os\nimport shutil\nimport time\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nimport torch.distributed as dist\nimport torch.optim\nimport torch.utils.data\nimport torch.utils.data.distributed\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\nimport torchvision.models as models\n\nmodel_names = sorted(\n    name for name in models.__dict__\n    if name.islower() and not name.startswith(\"__\") and callable(\n        models.__dict__[name]))\n\nparser = argparse.ArgumentParser(description='PyTorch ImageNet Training')\nparser.add_argument('data', metavar='DIR', help='path to dataset')\nparser.add_argument(\n    '--arch',\n    '-a',\n    metavar='ARCH',\n    default='resnet18',\n    choices=model_names,\n    help='model architecture: ' + ' | '.join(model_names) +\n    ' (default: resnet18)')\nparser.add_argument(\n    '-j',\n    '--workers',\n    default=4,\n    type=int,\n    metavar='N',\n    help='number of data loading workers (default: 4)')\nparser.add_argument(\n    '--epochs',\n    default=90,\n    type=int,\n    metavar='N',\n    help='number of total epochs to run')\nparser.add_argument(\n    '--start-epoch',\n    default=0,\n    type=int,\n    metavar='N',\n    help='manual epoch number (useful on restarts)')\nparser.add_argument(\n    '-b',\n    '--batch-size',\n    default=256,\n    type=int,\n    metavar='N',\n    help='mini-batch size (default: 256)')\nparser.add_argument(\n    '--lr',\n    '--learning-rate',\n    default=0.1,\n    type=float,\n    metavar='LR',\n    help='initial learning rate')\nparser.add_argument(\n    '--momentum', default=0.9, type=float, metavar='M', help='momentum')\nparser.add_argument(\n    '--weight-decay',\n    '--wd',\n    default=1e-4,\n    type=float,\n    metavar='W',\n    help='weight decay (default: 1e-4)')\nparser.add_argument(\n    '--print-freq',\n    '-p',\n    default=10,\n    type=int,\n    metavar='N',\n    help='print frequency (default: 10)')\nparser.add_argument(\n    '--resume',\n    default='',\n    type=str,\n    metavar='PATH',\n    help='path to latest checkpoint (default: none)')\nparser.add_argument(\n    '-e',\n    '--evaluate',\n    dest='evaluate',\n    action='store_true',\n    help='evaluate model on validation set')\nparser.add_argument(\n    '--pretrained',\n    dest='pretrained',\n    action='store_true',\n    help='use pre-trained model')\nparser.add_argument(\n    '--world-size',\n    default=1,\n    type=int,\n    help='number of distributed processes')\nparser.add_argument(\n    '--dist-url',\n    default='tcp://224.66.41.62:23456',\n    type=str,\n    help='url used to set up distributed training')\nparser.add_argument(\n    '--dist-backend', default='gloo', type=str, help='distributed backend')\n\nbest_prec1 = 0\n\n\ndef main():\n    global args, best_prec1\n    args = parser.parse_args()\n\n    args.distributed = args.world_size > 1\n\n    if args.distributed:\n        dist.init_process_group(\n            backend=args.dist_backend,\n            init_method=args.dist_url,\n            world_size=args.world_size)\n\n    # create model\n    if args.pretrained:\n        print(\"=> using pre-trained model '{}'\".format(args.arch))\n        model = models.__dict__[args.arch](pretrained=True)\n    else:\n        print(\"=> creating model '{}'\".format(args.arch))\n        model = models.__dict__[args.arch]()\n\n    if not args.distributed:\n        if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):\n            model.features = torch.nn.DataParallel(model.features)\n            ## model.cuda()\n        else:\n            model = torch.nn.DataParallel(model) ##.cuda()\n    else:\n        ## model.cuda()\n        model = torch.nn.parallel.DistributedDataParallel(model)\n\n    # define loss function (criterion) and optimizer\n    criterion = nn.CrossEntropyLoss() ##.cuda()\n\n    optimizer = torch.optim.SGD(model.parameters(),\n                                args.lr,\n                                momentum=args.momentum,\n                                weight_decay=args.weight_decay)\n\n    # optionally resume from a checkpoint\n    if args.resume:\n        if os.path.isfile(args.resume):\n            print(\"=> loading checkpoint '{}'\".format(args.resume))\n            checkpoint = torch.load(args.resume)\n            args.start_epoch = checkpoint['epoch']\n            best_prec1 = checkpoint['best_prec1']\n            model.load_state_dict(checkpoint['state_dict'])\n            optimizer.load_state_dict(checkpoint['optimizer'])\n            print(\"=> loaded checkpoint '{}' (epoch {})\"\n                  .format(args.resume, checkpoint['epoch']))\n        else:\n            print(\"=> no checkpoint found at '{}'\".format(args.resume))\n\n    cudnn.benchmark = True\n\n    # Data loading code\n    traindir = os.path.join(args.data, 'train')\n    valdir = os.path.join(args.data, 'val')\n    normalize = transforms.Normalize(\n        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n\n    train_dataset = datasets.ImageFolder(traindir,\n                                         transforms.Compose([\n                                             transforms.RandomResizedCrop(224),\n                                             transforms.RandomHorizontalFlip(),\n                                             transforms.ToTensor(),\n                                             normalize,\n                                         ]))\n\n    if args.distributed:\n        train_sampler = torch.utils.data.distributed.DistributedSampler(\n            train_dataset)\n    else:\n        train_sampler = None\n\n    train_loader = torch.utils.data.DataLoader(\n        train_dataset,\n        batch_size=args.batch_size,\n        shuffle=(train_sampler is None),\n        num_workers=args.workers,\n        pin_memory=True,\n        sampler=train_sampler)\n\n    val_loader = torch.utils.data.DataLoader(\n        datasets.ImageFolder(valdir,\n                             transforms.Compose([\n                                 transforms.Resize(256),\n                                 transforms.CenterCrop(224),\n                                 transforms.ToTensor(),\n                                 normalize,\n                             ])),\n        batch_size=args.batch_size,\n        shuffle=False,\n        num_workers=args.workers,\n        pin_memory=True)\n\n    if args.evaluate:\n        validate(val_loader, model, criterion)\n        return\n\n    for epoch in range(args.start_epoch, args.epochs):\n        if args.distributed:\n            train_sampler.set_epoch(epoch)\n        adjust_learning_rate(optimizer, epoch)\n\n        # train for one epoch\n        train(train_loader, model, criterion, optimizer, epoch)\n\n        # evaluate on validation set\n        prec1 = validate(val_loader, model, criterion)\n\n        # remember best prec@1 and save checkpoint\n        is_best = prec1 > best_prec1\n        best_prec1 = max(prec1, best_prec1)\n        save_checkpoint({\n            'epoch': epoch + 1,\n            'arch': args.arch,\n            'state_dict': model.state_dict(),\n            'best_prec1': best_prec1,\n            'optimizer': optimizer.state_dict(),\n        }, is_best)\n\n\ndef train(train_loader, model, criterion, optimizer, epoch):\n    batch_time = AverageMeter()\n    data_time = AverageMeter()\n    losses = AverageMeter()\n    top1 = AverageMeter()\n    top5 = AverageMeter()\n\n    # switch to train mode\n    model.train()\n\n    end = time.time()\n    for i, (input, target) in enumerate(train_loader):\n        # measure data loading time\n        data_time.update(time.time() - end)\n\n        ##target = target.cuda(async=True)\n        input_var = torch.autograd.Variable(input)\n        target_var = torch.autograd.Variable(target)\n\n        # compute output\n        output = model(input_var)\n        loss = criterion(output, target_var)\n\n        # measure accuracy and record loss\n        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))\n        losses.update(loss.data[0], input.size(0))\n        top1.update(prec1[0], input.size(0))\n        top5.update(prec5[0], input.size(0))\n\n        # compute gradient and do SGD step\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        # measure elapsed time\n        batch_time.update(time.time() - end)\n        end = time.time()\n\n        if i % args.print_freq == 0:\n            print('Epoch: [{0}][{1}/{2}]\\t'\n                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\\t'\n                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\\t'\n                  'Loss {loss.val:.4f} ({loss.avg:.4f})\\t'\n                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\\t'\n                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(\n                      epoch,\n                      i,\n                      len(train_loader),\n                      batch_time=batch_time,\n                      data_time=data_time,\n                      loss=losses,\n                      top1=top1,\n                      top5=top5))\n\n\ndef validate(val_loader, model, criterion):\n    batch_time = AverageMeter()\n    losses = AverageMeter()\n    top1 = AverageMeter()\n    top5 = AverageMeter()\n\n    # switch to evaluate mode\n    model.eval()\n\n    end = time.time()\n    for i, (input, target) in enumerate(val_loader):\n        ##target = target.cuda(async=True)\n        input_var = torch.autograd.Variable(input, volatile=True)\n        target_var = torch.autograd.Variable(target, volatile=True)\n\n        # compute output\n        output = model(input_var)\n        loss = criterion(output, target_var)\n\n        # measure accuracy and record loss\n        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))\n        losses.update(loss.data[0], input.size(0))\n        top1.update(prec1[0], input.size(0))\n        top5.update(prec5[0], input.size(0))\n\n        # measure elapsed time\n        batch_time.update(time.time() - end)\n        end = time.time()\n\n        if i % args.print_freq == 0:\n            print('Test: [{0}/{1}]\\t'\n                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\\t'\n                  'Loss {loss.val:.4f} ({loss.avg:.4f})\\t'\n                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\\t'\n                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(\n                      i,\n                      len(val_loader),\n                      batch_time=batch_time,\n                      loss=losses,\n                      top1=top1,\n                      top5=top5))\n\n    print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'.format(\n        top1=top1, top5=top5))\n\n    return top1.avg\n\n\ndef save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):\n    torch.save(state, filename)\n    if is_best:\n        shutil.copyfile(filename, 'model_best.pth.tar')\n\n\nclass AverageMeter(object):\n    \"\"\"Computes and stores the average and current value\"\"\"\n\n    def __init__(self):\n        self.reset()\n\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n\n    def update(self, val, n=1):\n        self.val = val\n        self.sum += val * n\n        self.count += n\n        self.avg = self.sum / self.count\n\n\ndef adjust_learning_rate(optimizer, epoch):\n    \"\"\"Sets the learning rate to the initial LR decayed by 10 every 30 epochs\"\"\"\n    lr = args.lr * (0.1**(epoch // 30))\n    for param_group in optimizer.param_groups:\n        param_group['lr'] = lr\n\n\ndef accuracy(output, target, topk=(1, )):\n    \"\"\"Computes the precision@k for the specified values of k\"\"\"\n    maxk = max(topk)\n    batch_size = target.size(0)\n\n    _, pred = output.topk(maxk, 1, True, True)\n    pred = pred.t()\n    correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n    res = []\n    for k in topk:\n        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)\n        res.append(correct_k.mul_(100.0 / batch_size))\n    return res\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "src/py3.x/kaggle/playground/dogs-vs-cats/models/AlexNet.py",
    "content": "# coding:utf8\nfrom torch import nn\nfrom .BasicModule import BasicModule\n\n\nclass AlexNet(BasicModule):\n    '''\n    code from torchvision/models/alexnet.py\n    结构参考 <https://arxiv.org/abs/1404.5997>\n    '''\n    def __init__(self, num_classes=2):\n        super(AlexNet, self).__init__()\n\n        self.model_name = 'alexnet'\n\n        self.features = nn.Sequential(\n            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=3, stride=2),\n\n            nn.Conv2d(64, 192, kernel_size=5, padding=2),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=3, stride=2),\n\n            nn.Conv2d(192, 384, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n\n            nn.Conv2d(384, 256, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n\n            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=3, stride=2),\n        )\n        self.classifier = nn.Sequential(\n            nn.Dropout(),\n            nn.Linear(256 * 6 * 6, 4096),\n            nn.ReLU(inplace=True),\n\n            nn.Dropout(),\n            nn.Linear(4096, 4096),\n            nn.ReLU(inplace=True),\n\n            nn.Linear(4096, num_classes),\n        )\n\n    def forward(self, x):\n        x = self.features(x)\n        x = x.view(x.size(0), 256 * 6 * 6)\n        x = self.classifier(x)\n        return x\n"
  },
  {
    "path": "src/py3.x/kaggle/playground/dogs-vs-cats/models/BasicModule.py",
    "content": "# coding:utf8\nimport torch as t\nimport time\n\n\nclass BasicModule(t.nn.Module):\n    '''\n    封装了nn.Module,主要是提供了save和load两个方法\n    '''\n\n    def __init__(self):\n        super(BasicModule, self).__init__()\n        self.model_name = str(type(self))  # 默认名字\n\n    def load(self, path):\n        '''\n        可加载指定路径的模型\n        '''\n        self.load_state_dict(t.load(path))\n\n    def save(self, name=None):\n        '''\n        保存模型，默认使用“模型名字+时间”作为文件名\n        '''\n        if name is None:\n            prefix = 'checkpoints/' + self.model_name + '_'\n            name = time.strftime(prefix + '%m%d_%H:%M:%S.pth')\n        t.save(self.state_dict(), name)\n        return name\n\n\nclass Flat(t.nn.Module):\n    '''\n    把输入reshape成（batch_size,dim_length）\n    '''\n\n    def __init__(self):\n        super(Flat, self).__init__()\n        # self.size = size\n\n    def forward(self, x):\n        return x.view(x.size(0), -1)\n"
  },
  {
    "path": "src/py3.x/kaggle/playground/dogs-vs-cats/models/ResNet34.py",
    "content": "#coding:utf8\nfrom .BasicModule import BasicModule\nfrom torch import nn\nfrom torch.nn import functional as F\n\nclass ResidualBlock(nn.Module):\n    '''\n    实现子module: Residual Block\n    '''\n    def __init__(self, inchannel, outchannel, stride=1, shortcut=None):\n        super(ResidualBlock, self).__init__()\n        self.left = nn.Sequential(\n                nn.Conv2d(inchannel, outchannel, 3, stride, 1, bias=False),\n                nn.BatchNorm2d(outchannel),\n\n                nn.ReLU(inplace=True),\n\n                nn.Conv2d(outchannel, outchannel, 3, 1, 1, bias=False),\n                nn.BatchNorm2d(outchannel))\n        self.right = shortcut\n\n    def forward(self, x):\n        out = self.left(x)\n        residual = x if self.right is None else self.right(x)\n        out += residual\n        return F.relu(out)\n\n\nclass ResNet34(BasicModule):\n    '''\n    实现主module：ResNet34\n    ResNet34包含多个layer，每个layer又包含多个Residual block\n    用子module来实现Residual block，用_make_layer函数来实现layer\n    '''\n    def __init__(self, num_classes=2):\n        super(ResNet34, self).__init__()\n        self.model_name = 'resnet34'\n\n        # 前几层: 图像转换\n        self.pre = nn.Sequential(\n                nn.Conv2d(3, 64, 7, 2, 3, bias=False),\n                nn.BatchNorm2d(64),\n                nn.ReLU(inplace=True),\n                nn.MaxPool2d(3, 2, 1))\n\n        # 重复的layer，分别有3，4，6，3个residual block\n        self.layer1 = self._make_layer(64, 128, 3)\n        self.layer2 = self._make_layer(128, 256, 4, stride=2)\n        self.layer3 = self._make_layer(256, 512, 6, stride=2)\n        self.layer4 = self._make_layer(512, 512, 3, stride=2)\n\n        # 分类用的全连接\n        self.fc = nn.Linear(512, num_classes)\n\n    def _make_layer(self,  inchannel, outchannel, block_num, stride=1):\n        '''\n        构建layer,包含多个residual block\n        '''\n        shortcut = nn.Sequential(\n                nn.Conv2d(inchannel,outchannel,1,stride, bias=False),\n                nn.BatchNorm2d(outchannel))\n\n        layers = []\n        layers.append(ResidualBlock(inchannel, outchannel, stride, shortcut))\n\n        for i in range(1, block_num):\n            layers.append(ResidualBlock(outchannel, outchannel))\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.pre(x)\n\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        x = F.avg_pool2d(x, 7)\n        x = x.view(x.size(0), -1)\n        return self.fc(x)\n"
  },
  {
    "path": "src/py3.x/kaggle/playground/dogs-vs-cats/models/__init__.py",
    "content": "from .AlexNet import AlexNet\nfrom .ResNet34 import ResNet34\n"
  },
  {
    "path": "src/py3.x/kaggle/playground/dogs-vs-cats/使用迁移学习进行猫狗识别98%.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 根据Pytorch迁移学习教程改编而得\\n\",\n    \"原文地址：\\n\",\n    \"\\n\",\n    \"http://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html\\n\",\n    \"\\n\",\n    \"使用迁移学习进行猫狗识别\\n\",\n    \"==========================\\n\",\n    \"\\n\",\n    \"这个教程将教你如何使用迁移学习训练你的网络. 你可以在 cs231n 笔记中阅读更多有关迁移学习的信息.\\n\",\n    \"\\n\",\n    \"http://cs231n.github.io/transfer-learning/\\n\",\n    \"\\n\",\n    \"引用自该笔记,\\n\",\n    \"\\n\",\n    \"    事实上, 很少有人从头(随机初始化)开始训练一个卷积网络, 因为拥有一个足够大的数据库是比较少见的. 替代的是, 通常会从一个大的数据集(例如 ImageNet, 包含120万的图片和1000个分类)预训练一个卷积网络, 然后将这个卷积网络作为初始化的网络, 或者是感兴趣任务的固定的特征提取器.\\n\",\n    \"\\n\",\n    \"如下是两种主要的迁移学习的使用场景:\\n\",\n    \"\\n\",\n    \"-  **微调卷积网络**: 取代随机初始化网络, 我们从一个预训练的网络初始化, 比如从 imagenet 1000 数据集预训练的网络. 其余的训练就像往常一样.\\n\",\n    \"-  **卷积网络作为固定的特征提取器**: 在这里, 我们固定网络中的所有权重, 最后的全连接层除外. 最后的全连接层被新的随机权重替换, 并且, 只有这一层是被训练的.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# License: BSD\\n\",\n    \"# Author: Sasank Chilamkurthy\\n\",\n    \"\\n\",\n    \"from __future__ import print_function, division\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.optim import lr_scheduler\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\\n\",\n    \"from torchvision import datasets, models, transforms\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import time\\n\",\n    \"import os\\n\",\n    \"import copy\\n\",\n    \"\\n\",\n    \"plt.ion()   # interactive mode\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Load Data\\n\",\n    \"---------\\n\",\n    \"数据集地址\\n\",\n    \"\\n\",\n    \"https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data\\n\",\n    \"\\n\",\n    \"# 比赛说明\\n\",\n    \"+ 在本次比赛中，您将编写一个算法来分类图像是否包含狗或猫。这对人类，狗和猫来说很容易。你的电脑会觉得有点困难\\n\",\n    \"\\n\",\n    \"## 特征说明\\n\",\n    \"+ Dogs vs. Cats是一个传统的二分类问题。\\n\",\n    \"    + 训练集包含25000张图片，命名格式为..jpg, 如cat.10000.jpg、dog.100.jpg\\n\",\n    \"    + 测试集包含12500张图片，命名为.jpg，如1000.jpg。\\n\",\n    \"+ 参赛者需根据训练集的图片训练模型，并在测试集上进行预测，输出它是狗的概率。\\n\",\n    \"+ 最后提交的csv文件如下，第一列是图片的，第二列是图片为狗的概率。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 数据目录结构为\\n\",\n    \"+ train\\n\",\n    \"  + dog\\n\",\n    \"  + cat\\n\",\n    \"    \\n\",\n    \"+ val\\n\",\n    \"  + dog\\n\",\n    \"  + cat\\n\",\n    \"+ test\\n\",\n    \"  + test\\n\",\n    \"  \\n\",\n    \"其中使用train中dog和cat各2500张图像组成Val数据集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Data augmentation and normalization for training\\n\",\n    \"# Just normalization for validation\\n\",\n    \"data_transforms = {\\n\",\n    \"    'train': transforms.Compose([\\n\",\n    \"        transforms.RandomResizedCrop(224),\\n\",\n    \"        transforms.RandomHorizontalFlip(),\\n\",\n    \"        transforms.ToTensor(),\\n\",\n    \"        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\\n\",\n    \"    ]),\\n\",\n    \"    'val': transforms.Compose([\\n\",\n    \"        transforms.Resize(256),\\n\",\n    \"        transforms.CenterCrop(224),\\n\",\n    \"        transforms.ToTensor(),\\n\",\n    \"        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\\n\",\n    \"    ]),\\n\",\n    \"    'test': transforms.Compose([\\n\",\n    \"        transforms.Resize(256),\\n\",\n    \"        transforms.CenterCrop(224),\\n\",\n    \"        transforms.ToTensor(),\\n\",\n    \"        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\\n\",\n    \"    ]),\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"data_dir = '../dogvscat/'\\n\",\n    \"image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\\n\",\n    \"                                          data_transforms[x])\\n\",\n    \"                  for x in ['train', 'val','test']}\\n\",\n    \"dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\\n\",\n    \"                                             shuffle=True, num_workers=4)\\n\",\n    \"              for x in ['train', 'val']}\\n\",\n    \"testdataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\\n\",\n    \"                                             shuffle=False, num_workers=4)\\n\",\n    \"              for x in ['test']}\\n\",\n    \"dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val','test']}\\n\",\n    \"class_names = image_datasets['train'].classes\\n\",\n    \"\\n\",\n    \"device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 可视化一些图片\\n\",\n    \"\\n\",\n    \"让我们显示一些训练中的图片, 以便了解数据增强.\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x23581f812b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def imshow(inp, title=None):\\n\",\n    \"    \\\"\\\"\\\"Imshow for Tensor.\\\"\\\"\\\"\\n\",\n    \"    inp = inp.numpy().transpose((1, 2, 0))\\n\",\n    \"    mean = np.array([0.485, 0.456, 0.406])\\n\",\n    \"    std = np.array([0.229, 0.224, 0.225])\\n\",\n    \"    inp = std * inp + mean\\n\",\n    \"    inp = np.clip(inp, 0, 1)\\n\",\n    \"    plt.imshow(inp)\\n\",\n    \"    if title is not None:\\n\",\n    \"        plt.title(title)\\n\",\n    \"    plt.pause(0.001)  # pause a bit so that plots are updated\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Get a batch of training data\\n\",\n    \"inputs, classes = next(iter(dataloaders['train']))\\n\",\n    \"\\n\",\n    \"# Make a grid from batch\\n\",\n    \"out = torchvision.utils.make_grid(inputs)\\n\",\n    \"\\n\",\n    \"imshow(out, title=[class_names[x] for x in classes])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"训练模型\\n\",\n    \"------------------\\n\",\n    \"\\n\",\n    \"现在, 让我们写一个通用的函数来训练模型. 这里, 我们将会举例说明:\\n\",\n    \"\\n\",\n    \"-  调度学习率\\n\",\n    \"-  保存最佳的学习模型\\n\",\n    \"\\n\",\n    \"下面函数中, 参数 ``scheduler`` 是\\n\",\n    \"``torch.optim.lr_scheduler`` 中的 LR scheduler 对象.\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\\n\",\n    \"    since = time.time()\\n\",\n    \"\\n\",\n    \"    best_model_wts = copy.deepcopy(model.state_dict())\\n\",\n    \"    best_acc = 0.0\\n\",\n    \"\\n\",\n    \"    for epoch in range(num_epochs):\\n\",\n    \"        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\\n\",\n    \"        print('-' * 10)\\n\",\n    \"\\n\",\n    \"        # Each epoch has a training and validation phase\\n\",\n    \"        for phase in ['train', 'val']:\\n\",\n    \"            if phase == 'train':\\n\",\n    \"                scheduler.step()\\n\",\n    \"                model.train()  # Set model to training mode\\n\",\n    \"            else:\\n\",\n    \"                model.eval()   # Set model to evaluate mode\\n\",\n    \"\\n\",\n    \"            running_loss = 0.0\\n\",\n    \"            running_corrects = 0\\n\",\n    \"\\n\",\n    \"            # Iterate over data.\\n\",\n    \"            for inputs, labels in dataloaders[phase]:\\n\",\n    \"                inputs = inputs.to(device)\\n\",\n    \"                labels = labels.to(device)\\n\",\n    \"\\n\",\n    \"                # zero the parameter gradients\\n\",\n    \"                optimizer.zero_grad()\\n\",\n    \"\\n\",\n    \"                # forward\\n\",\n    \"                # track history if only in train\\n\",\n    \"                with torch.set_grad_enabled(phase == 'train'):\\n\",\n    \"                    outputs = model(inputs)\\n\",\n    \"                    _, preds = torch.max(outputs, 1)\\n\",\n    \"                    loss = criterion(outputs, labels)\\n\",\n    \"\\n\",\n    \"                    # backward + optimize only if in training phase\\n\",\n    \"                    if phase == 'train':\\n\",\n    \"                        loss.backward()\\n\",\n    \"                        optimizer.step()\\n\",\n    \"\\n\",\n    \"                # statistics\\n\",\n    \"                running_loss += loss.item() * inputs.size(0)\\n\",\n    \"                running_corrects += torch.sum(preds == labels.data)\\n\",\n    \"\\n\",\n    \"            epoch_loss = running_loss / dataset_sizes[phase]\\n\",\n    \"            epoch_acc = running_corrects.double() / dataset_sizes[phase]\\n\",\n    \"\\n\",\n    \"            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\\n\",\n    \"                phase, epoch_loss, epoch_acc))\\n\",\n    \"\\n\",\n    \"            # deep copy the model\\n\",\n    \"            if phase == 'val' and epoch_acc > best_acc:\\n\",\n    \"                best_acc = epoch_acc\\n\",\n    \"                best_model_wts = copy.deepcopy(model.state_dict())\\n\",\n    \"\\n\",\n    \"        print()\\n\",\n    \"\\n\",\n    \"    time_elapsed = time.time() - since\\n\",\n    \"    print('Training complete in {:.0f}m {:.0f}s'.format(\\n\",\n    \"        time_elapsed // 60, time_elapsed % 60))\\n\",\n    \"    print('Best val Acc: {:4f}'.format(best_acc))\\n\",\n    \"\\n\",\n    \"    # load best model weights\\n\",\n    \"    model.load_state_dict(best_model_wts)\\n\",\n    \"    return model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 显示模型的预测结果\\n\",\n    \"写一个处理少量图片, 并显示预测结果的通用函数\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def visualize_model(model, num_images=6):\\n\",\n    \"    was_training = model.training\\n\",\n    \"    model.eval()\\n\",\n    \"    images_so_far = 0\\n\",\n    \"    fig = plt.figure()\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for i, (inputs, labels) in enumerate(dataloaders['val']):\\n\",\n    \"            inputs = inputs.to(device)\\n\",\n    \"            labels = labels.to(device)\\n\",\n    \"\\n\",\n    \"            outputs = model(inputs)\\n\",\n    \"            _, preds = torch.max(outputs, 1)\\n\",\n    \"\\n\",\n    \"            for j in range(inputs.size()[0]):\\n\",\n    \"                images_so_far += 1\\n\",\n    \"                ax = plt.subplot(num_images//2, 2, images_so_far)\\n\",\n    \"                ax.axis('off')\\n\",\n    \"                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\\n\",\n    \"                imshow(inputs.cpu().data[j])\\n\",\n    \"\\n\",\n    \"                if images_so_far == num_images:\\n\",\n    \"                    model.train(mode=was_training)\\n\",\n    \"                    return\\n\",\n    \"        model.train(mode=was_training)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 微调卷积网络\\n\",\n    \"加载一个预训练的网络, 并重置最后一个全连接层.\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model_ft = models.resnet18(pretrained=True)\\n\",\n    \"num_ftrs = model_ft.fc.in_features\\n\",\n    \"model_ft.fc = nn.Linear(num_ftrs, 2)\\n\",\n    \"\\n\",\n    \"model_ft = model_ft.to(device)\\n\",\n    \"\\n\",\n    \"criterion = nn.CrossEntropyLoss()\\n\",\n    \"\\n\",\n    \"# Observe that all parameters are being optimized\\n\",\n    \"optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\\n\",\n    \"\\n\",\n    \"# Decay LR by a factor of 0.1 every 7 epochs\\n\",\n    \"exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 训练和评估\\n\",\n    \"使用 GPU 的话, 需要的时间约为70分钟.\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"NameError\",\n     \"evalue\": \"name 'train_model' is not defined\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mNameError\\u001b[0m                                 Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-2-cc88ea5f8bd3>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\\n\\u001b[0m\\u001b[0;32m      2\\u001b[0m                        num_epochs=25)\\n\",\n      \"\\u001b[1;31mNameError\\u001b[0m: name 'train_model' is not defined\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\\n\",\n    \"                       num_epochs=25)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x178677a0048>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x178646bb6d8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x178678f2e80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1780af5aef0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1781cb6e0b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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AWRd7HNWHt6pO9Z6Zp+MDTT6Mixa27N3n9rZukgzFRlFKVcz79q5/mhVHc1wpIhbMOJy0qDjnY38cVWc/tgJaSKAgwrUMHmiJLmRs4nk6p65ooDpBCYIzFOUuahDjnuXHjJs7B7s4209mMznSnModngiMfrvgkSciyjDzPSdN0Y+yvViv2Dw6oqorRaESWZRuz4mEgOtABg8FgrRwZlPYIYXDe0bQNLzz3FB966XmyQU5nHcuqwzlFrEOENXzpS18iRiJlb3po5xEClJCAYlFbjIA4S3HWopTEesOyXeE0LFeWsqxp2yUXL+1hjWA2mxHoBLzsFa8ORqMJAAdH96nqJZPx+JtPzjukMwFkFEUbA/pR8yHPc/I8Zzgcsr29vRGRYRBsxOjJ9IQo7AFP0xStNEKKjbEuupon9napli3zsmG2qFg2hjsPDplMJmiladuW+7feJMszGqOoVdhrwcJsXGphGG0C1NbaTXhsPB5jvefpK7sMQ8+0LLl9+xZZlhFGEUEQcPHCBZIkQQhBnucbMR0EAbP5/FTm8EwAmaYpaZpSFMWGE6F31Q0Hgz6Y3HYU6xBVb1j3e+XW1hY66J3fTd0QRmE/uWvF56krlxmlCXg4Op6hghCPIEp7MbmqVrz6yivEYchwOOTuwSF39g+xeMJAbRzz4GEN6kOnuTGGLMtwzhNruDgZUte95uu9Z3oyZTQccvXatY3GWhQFQRBQ1zXj0WjjQ363dCb2yNGkFy8SCLSm6zqOZjNq0+KVIE1SFssFxsbERUZlOqbljOEgx3Udy3LOg9tvcTyfE8QFcRKzt7vHnbs3iXTIsPAoGdK1hldffw0VBijdh7DevnmDZr7k3/qBfxkfRfztV15hVToO1T7PXNwhCjVeChb1AkWCsZ4sSfFe0NYNWWKIlUcFmq29S3zl/itoIciKHGsdhyfHjLe3GI6GHJ+c8MZbb6ICjW0Uk+09jqenk7NzJoA8ODwkzzKyJKTtDMZatre2mM3nPScWvULjnKMs59SLJQGSr/zmZ7l78zbSeYaxJtQhdtlw1LXcfeXLBEHAP379NbzUPPOeFxhv7xBIQRDHoARN1/DWa2/wJ7/re9jaukKWpfz1n/pv+Vs//dO8/OXf5s39A56/fpVVXTHMcubHUybjAcmgoGka0jTFGEMUJywXJVGUkgbQdp4oDDmezvo0k7ZlXpYMh0OaptmE3B5y9WnQmQASYFUtca7i+OiYLMvIcsGgKLh56xazeW9Mn5yccHL3K5hlTWQ8SgdcyhKcsSjtCBNNuWoYBpKqcyhvGKchVddy65XP8+Xfbqid5sX3vY9rTz3Bb/zGbzCIUz72Hd/F/dLgI810MeWHP/EDNM2KN27c4N7xIeM0Q3nBxYsXEUL0JsU6BaSqKnQY0bUtwywjEA6jFFmWUS5XzFcrjk9OUEpx//59wjBkb3eXxWLBjRs36LrT0VrPBJCLxYI4iijiIUGcUNYV5fIu7WLO9N5dDhdzQqEQzlNo0adgRJphErJcLQmidTqGNSQawkAREmKNQQUSa+BKkVBpwY3pkq988Ys8OJpSTme8+OSTzGfHZLpABzHOxJjE8WP/7r/Pj/+ln+BgahkFDSSaw6rhiWSEd5buoW1qDEWeMoti6g6ujAbMdUY5nTNSnloHNKsah0dqT2tq3rzxBoGOubCzy3z/6FTm8EzUfvwXf+Uv+QcHh0xPpgxCOLp/hyIyYD2R0ugoZDmd065qVPBVT423hjB8aPT3DoLlcsl4NEKuOaepa0DQ1jVhHLFooTSwPy+RCr7no9/HH33vh4jyLVRW4O2Kzhnq4yNUFvLn/7OfROF57zPXWDQtl4qUQRoRZ30mXJ8QrfBhzHy+JNKK33ztJqO9y3jT4IUikgpbt3znd3wQhOAzv/Zr3DyaYV2fFvlbv/WF85GgvFpVbE1GmMUJq7tvcj2LMGGADmO2ti5QLxYMopSj/QMkfZZaXdcgNWEYgIfVakWRpWA6vGlpuj5nddVWxFEI0jPKIsaDiFsHJ+hRzmw+5YWnniaJEjrvwFrCIECHOb5aUddzLmyPuXc8p+56s6harhjlySarPY5jTNfgraPrDHkU0FQl9x7sk0QhTz/5JD/4p/408/0joixFBwEf/vB38+M/+RcR3jNfniPPznw+ozs5oDy6x1aqKYoUqWOibIgOUsKBwJzMifd2mB4dEIQhQimEZRO8HQ0HfWhqPEJ4wXw2p60aBvmIOI6pdEXbeqyoKbIQ1VTsXL6EwKBFghAtQaxRLmK5KnFxgKgl/81P/AV+7Cd/gtdv3CLMCqbaUUyG+M5StxYdeBrjSbQgDTTOW5I8oybgT373d/Ds9fdQlUuiYoiIUtr5CWUwQwQa6Q36lDw7ZwJI1VWcHN3l0tYEYRuUDtndvUDV1GAqBB0Wx3A8Ynp0gFYaGQTESjMvS4Ig2qT+AwyKnNVyQZoWGGOoqiVRFLFcLnHeUwwLlBaYpuPgzh2evPReinTA4bxktJ2QyQSlDJY50we3+Td/8BP8k1/4RVoipvOSl195C+0dHti7eJXxcEgSBNTWEoiQS8MJP/QjP0o1PcLKAKVCUL3kiKN44+w4mR5tbOZ3S2cCyOndt7m2nRJIy2h7F6UUq2pFGgVU5QnW1uxtXcQGCePRiMaa3jmwnJGkAYHWCAKyNEUpxfHxAZcu7VAuFtSzBVvb4167DMBbj8aisYRKEzlLpwyR1wziBJlIYtmyPDlGMuOkXPHU089w841XacsZ0YUdTNtQVzVOBlQ47tx8nS+9WpEkKePhkP/kx36MW7cPiLIRwgqirMA4EHiE6+i6ltFwSLmYEYanA8GZAHJ3ECMCidQ5RTagrlcEWmG7tk/RlwqjJcpXTHa2uXHzJoUH7xSB1tR1Qxw4VlVLHEVEYbAxDUbDIctFb3SPhgNWK4NEszMacDyfkg8nRDah7WYgFCc3bjNfHZBFkmqxpFxOCY4Ml7bHmO0x9+7eYTgZM/GeeblkOyuYZAHzeslkd5ePfewHuH/7JsgBYZiAEmjvEdZTtw6F5NVXXyUtcqI4RelzBGQcJwzynFAprGnQWOK4QEuP7WqcASc0YSiJvWRQFBwdHzMcDBBCkGUZbb3AOodZJzM559jZ3qbrWiDdJEGFYR+Yruua4XBEMYgxdoE1hrdvvIkQjr3L16lnh9jWYhcVs7qm0BE+jbm0tc2Nt9/AO4vH07UdW9sTLsa7PPOeF7j11lskeUG+la7zZiEgYFEuuLC7x2J1yM/+o3/Qh7zGY6I4PpU5PBNAZmlOlg6xTYn0liSOqNoWpfsM8yjNSMdjMF1fIhCGFHnexwWbhrppiAOBXgeYH+bBlIsFSZwg11nh/X6a4Zwjz3NQGuc7siLgjdduc/nSDtYsmc/uMk4jRkmO0o7ZyRFREhFFCVkccXFnh6qpCcKIbDhBIFCB4+D+HcJ4i6auCZsGFViyKKRaVuxtb7Nalvz253+TVbciFTlunTB9GnQmnOZZWqyjFhoZSGpDD1oYUgy3MFJxdHLM8X7vCZnOZn22uWsQ0iKl3ZQLRFHEweEhh0dHPbBBQJrlKB0QJynjcU4UK6q6xNHw5de+gBeCp554iq5tCbKIIvNI2TFfLbFU6FgSjwqq5QGztkRnI5Ks4PKlK1ze26FazugaQ6hivO3IihxsjalmtIuGIk6Ylycsjo/42Z/7e8QqJAtT6lVL607Hjj8TQGrVRyGSOGG1XCKEIM0y5vM5pjMMRxO0tUih6NqOJI6xzrFYLjGPlMdprTcp+EkcU9X1Jmn5YZ2HMYYkjhkUBZPBkEGa40zJ0f4tlstpnwPkHIvFgqZpsNZvssoHw22yYsCiXqJ0SJgk7B8eYR1cunKNKEmI0gRnO1arBQ8e3MPahlVVEkaaT332V0FJkjin6zpW1Qp3SnmtZwJIpRTD0QjrLGEYEicJgdZ9PLLIMa0jcjWDbMiqWm2SqaSUBGtRatd1i50xTMZjtNZ9hsE6k0BrzdZkQhj2Ya44jhHOk4Yxr7/66xwfv00WQXl0sKkRUUoR6pTJeAdjBIaMO/ePGW5NMEjmy5rOCa499QzzqmFRdYhAowPBeDLgiaeu0pkaMFjbsPPELnsXLvLElac3JX7ulDxrZwLIwWhCVbU4ZxAqYrmqIE4IBwW+6TCrEq0i4nxAXS+QIkBrQRJnSKHBS8bjbVZ1S5LkBGGfbZ7lGTu7FwjjnPHWHjpIaBvDbL5EBhHOtDjVZ94NtoYEaYhQgtZLfJiyKmeMBgVGRFRlSbhzkWp6RBz3CVPbOxOSJCYMQ8K0IM4HmLpitZyjlcPainI+R/qQMAv5Rz//c9y/fwBKULcVg6Lg4u7FU5nDMwFk62pGkxS8JwxiRpMxTVNTr1YsyynWNsgk5mQ6JwwjjO0rqpTUKBUQxymdsSgVsLt3gSAMCcIQD3TGUNc18/kcpTVFPkDrkDCMGO7s0VqLkpqWgHLVEcQFk9EWoQQtYHp0H1/P8fWMPA2IbEU5myFFwKycsbWzxYP7+wyKAiU8sZZEYUhZzpieHCJFi9CWv/MzP0PXGaIwZraYUbV1Xy4ozlE6pNYR7TrxKoj6SuQk1LBcoCNNmMZ0eOIipa4NUlnCINlULT+skXxYbt4Zw2KxIIoi2rbBOUPXNYBba7SevCjoCInihOnJEYHriKVH25ZWj9i5fJ3RM+8lGozRAVy8/gTWdGzvXUKEOVFe8ODgmGI0oekMzXKOxNC1DXodHI+iCNfNsHbG73zl1c0x1hVlzjusPUfxyGx8GWEbbBKSaElVGQJRo/CIKEXogK7pWFQlOnC0BlSsCeKcblnSrkqE7t10vRuuV26CICBQYV+8KiVBEFIvp1hbcfvWPstkxE5kKKuK1fHnyGXK8Okn0bdexrZ3mN1fMrj+LFde+iCmctx7cJfh1WeIhtcJpMGKJXQhReLR4YDj/c8RjZ6l7gJcZ2iqI1SiuHHrdbxzeDyXLvSxyNFwQhCGLJbnqKvH9sVLREmODkPatgGpsc5jgNpAlg82uax9OqJAByHGdKyqqhe3xpAkPZfGcbwpudNKgxeEQcRquSJOc4J0QD7Z4/ozL+K9ZzLeIhuMuXDpCpOdHaJ0wGFZ8tTz16mWd3nt0/+QNBkwLEYkgyF4RxKNyEZ7+LjApCPyrae58tIfYbx7iclwTDLK6bRlPp9zeHyCFx7jPZ01nMymiLWmzbsOYPV0JoBExYTJhCAcgg4JUo3xEq8CssGY6XS6KQMIg4TRcJu2sbi6JIlD0H2jh+VyuWnP8jCSv1qtaNuW2WzW+3BtCOlViieeI92+jNQhR4f7jPeuY7OU26+9zjPPvoQR27x1NGOUx8wOV8T5Hjt7T6HCnHSQsb17ma2r7+H6U8/w/FOX+IB/mdmXPwVv/Aqz/VvMD+8wSBSL2Zy6beiE58mnnmXVGFQQM5vPyYvifDkEEAGT7YsMhhMsEMYhOoxI8wLrochz5vMZTdtgjEPLEKUChDWEQUhRjDaRj4d5rl3XUZblprRcKYXSmsFkh8HOFfauP8lkZ4/RaMzO9g6dF8g44srly7zx1pt8+CPfz2wR8ubv3MAMP4wIElAh1vWdPg7LfbYu7PKZ//fnaU7uMzv4Ejcf3GO5KHny+hVGQvHqp36N1WLJYlHipeDWvXtEaUrVdQyKgiiKODlPZXVCwbSzkMS4A0NbrhCAE4pY+XVTJMFkawvT1VgviGOBdTFZliPDPkdGConzjmpZYW2fsZ4NY4xtyCdjPJJgtEex8wRFsYszDdF7PsQrv/My13d6M2A8mYAumFy6zL/03Acx1vPyL/wci+lt6tWM6e038WHKlRe/k9AaDg6n3L9/g+mFPbZHI6688BGGexNu3b3J7nte4vXbb3HnwQGhD9geT/DGopGMxltMp1OkOh078kwA2RvsAXbtKwW/qe3ou15IinxIU/dc12ujHU6FRFlBaxxd15HECc460jzvHejGYIHOOSJCPIrB1iWIc5TU6FiiBlvsPfk8QRYwL0u2syHZ1hMEozFChSjn+cCHP8yvfPIfIFVIgKFZGaZtyWW2ufD0JV744A9RHb3JuMh565XXuPXZX0MFAUGYgSqYl/vgO8IgwBrDaDxiuVwShposy05lDs8EkNUVT6KBAAAMWElEQVRyidYKFQQUxYBpWRKHIVVVEYUhbesQShMlCmc9pjHUbU0c51R1Q5QVm3YogQ4QSlO3LV4orLAEUYR1Ch3lWJmQxQVZltLajjS5SFRsEQSKS0nKoiwJwgApFEGgWC2mfP7zv8GiXJKFNfgOryKOXv0FPjf6GB/6499HFlxiNVGUh7fJdlKOF4r9+RxHwsGspsh32J++ipSSeVkymUxI05z5/IQ0SU5lDs/EHtnVFVgHKqYREVmWg5YgHFpa8khhcDTditl8BlKQ5TlGSJCeZnFMHMVYZ1lVKxbzGc1ygW1q7MoReU2WJexef4IkSykyjZKi7y3XWfIkw3UOjSJPC1or8FJj6xmvffaXMYsThKkR7Yp4uEeaDakPHeXR51gcrwhETZZtExeXEUFOUFwgTCc4D/lwwmTSOyjGozFd02I7QxxHQIcU56iF2WJRImSIjGLUOjRULVsCFbFcNrR4lFSI1pLnBUIKTNfXZSRxQlM3GFtjXU1nKoxpCYKAPM9JBkMaqVhajwt6s0QrhRKSNEkJo34i0yyi7WrqesmqPKKeH9Ac3efyJOPC5S3sqiEtRgy3dsnHOyzv3yb3LQ/e+jxvfvnXcbUljAZYNWDr6nvZvvY+Sp+zf7CPUoqiKJienODXFWXlfM7R8TFBeDqpHmcCyIOju9y+8yptZ0EG1J1F6wjrBGmSUbV175GxblNe7rwjTmKatm9B1tSeUA/wNiBNi00rl2XTgA7QaUonxMZJXdU1VV31+6r3COmJ44CmXWJtTbcqaWZTomxINN5GhEMyFUNjEbWls4rBaBulFZ2pWKwOuHf/Jtkg6zPxpGJUFAgEi7Jkb3eXk5Ppxruzf3BA1xlMdzrmx5nYI5vaUs4O6JYrpvMjwCG8ZzmboqVne/cClbHUQUhkJVI6AhkQSYlzoIIYR0c6GmCloFpOEVi8hyKJeyBFzN5wAjogWrd4CUKJUI4kC2Fd2Lq3e5EH9+/jnaGRUNUzwmSHj33iE3z+138J9eXPs9ye8PE/+2/T1BXl4gSijNffvkGWZhiV0BhLmGYESYrUirY2aKd48sKAL74xI84ylvNj0jhneUp25JkAcjwes1wtqYVj99pVhsMBptN42/LqF7/IqzfvkoeanckIhMcbR5xFKKVZlAsGwwGdaTk+OeoLZYUANGmScDwviYsQLTVVayjyvsWn6TqCsK+/EAha2/dNreqK8WiAqVfoZEA2yGg6j452uXjpCd44avjDH//XSLPLdLalaFd0tqG8cQPn+uZPD32teZ5zfHJCEglOTg6JIsXzzzzDvQcHZGnCbFGxc+F06iPPBJDPPvccDx7s8+DuXU72p7TLhmwwJpDw0kvv43hZU8QBbb3i7S9/ga3RgM5apPQkaYK1lqoqsbahbWuM9URRwsm8JIpziuGE4dY2XoUkSYqgD1yDpa0bEIIoDBFSYI1F+o4u0MzLJSIb4ad3mR3s4ycv8oc+/m2YIGdlSsCxaKacnEzZ3rmIVorDo0OGo8Gm191kMqFrSvZ2dqnbjpMH+8TOcnRwSBilJKfUr/VMAPlPfvkXCHTAk1efpBis/arO902Luo6RjCnLkjAccOGZ93Fw8zW2Y6iNJ9QKj2OxqPEerBUUo23saoZtaqJ8jG9KYleTpwlCCqSSfZ1/Z9DrPgPGdgRyXW4HKOkYjgbcP7hDM1/iFiuEq7k9FQytJ8pC6rrCWsfWaJt5WRLFEVtbI4TsHfZKKXa3tjk69iwWUxZNjdQCIRRb+YiTebneSt49nQllJ9ARRd77Sl995RVu375NGIV9V6t1oUxfUu7I8gHXn32B1+6dYIKMVdd3Vg7XdmeSJJhmhe1qxtu7RMUEnY3R6RgZ5njne0c6fYGtdXYdGQmQ6/7lArFpyrQz2WKytUU+GZNuDUnHCW/d/EJv0AchaZbSdR2TyZhBMSBJU6SQ1OsWZ9nuLp1xiMaTr3vPBUGAbVrGxZB2dTqtsM8ER+7sXGB6csIgy8myjDRJeOP117l6tW9i+zC1o2/0F1IuG649935u3bnBbhbQNRVSqk16R1OvGOU5QgWEgULR4bslwjZEcb4pHVcKTNeHvHD9vTzsg6cDTdf2WXtxluLwVCtNkSguWMl4Mu6Bl30BURRHKCkpyxKtFYNiwKIsefraU6RByNtvvc6De7douw7ftmQq7NuHqnMkWpXWPP300xjbceveXUSgSaKAxfKYJAlR6qttUaRdESrHsqq5dOUpjm6+SqEFpl6iAg3OkGcFSoB3HUUaspzPqMt92JcUWY6IYoStaV3fxN7jN01+nXeA6DlXa7rOoxSkWUExGOG9o+kMHo+Ogg2YgQ5w3hGFEcu6o7Mt1fweg2zMEx/+KBcv7vFLv/RJwqN7rKqWsq2RtiVL01OZwzMhWouiIIxjhqMR73//+7l58xbl8pCDg0O6NsCYDmN6B0C5XHF0fIIXPQhPPvcCd0+WSC2JlMQHCS5I6LyldYbFckHTtnhncdbS1nOE8LStXcc2fb8fO4/zDr9OTxRS4Py6Q7P86jNEAKI4Ik4SpOibOAW6z6MVCKSWaOkpj+4hmxJfHSLEiq2dSyRJr5hleU5R5KRJQntKha5nAsi93T3SdTB4MBzyx7/3e3nfez/E/v4DvvDFz3JycsJqtaIsSwyafLTNaPsCzoNKBnz7R/8EVfEEjRPIboXUgsZ0ZIOCpu36jDkhQEr8ctZ3bAwjPH5TBg5sQPTrWkvow2KCXkHyvs9kD4MQvU6UfthazXuPW5s1Xb0ipSGxJeb4DfbvfJEkn7C3t9e35qZPy+yM6dubngKdCSDjOCBJY5IgJI/iHtQk5/s+/gPsXHiC3/6dT+N83ddCSkU+GBLGCVo7dABJlvCe934bevsprE5wq4YwGGJqTxpHtB7qtqE6vsdxeUKIRcp+U/S277LlJTg8Dk9fE62R6E1TX9MZnPMb8aulItQBSsi+abCUdM4yLWec3LuBWN6nmd9D44kHY0LleP7qk9B0hPWMGAi8xLrT2d3ORMXyyy//ug+kQvAINwQK2xmsc9R1x8svf5obN9/gfe/9duS6104UgHc9IJEU3L59hwcHRwRBRJokNMuSowdv820vPodpKnZ3tgnGT7B14WkGO1sEYbzOxpO4R55NLRGbJg1t1zcrbNpm05HLGUuaFkRx1HfHctAsF9x78wvs33ubRVMxGE1ACE6WDcumJYoS2tayWs2oVgtee/ll2rbFOvh7P/+p81GxLKXEAfKhaKMXU9b1WWbWt1x/8hmG4wl4yXAw4OTkhLbuOF63NFMqIh/v8MR4F6lDhPd423LpiSdYLqasliVbuwFpELDuz9w7AKxFSdnbj+snDTwE8SE3Ou/6+g4lcc7jZH9cCklnO5QSvPLll7HdgqvPPY1VIXXjqFYNk8xxSUhsIGiqltfeXPLMi99GHo/41C//It41pzKHZwLIpmmIoxjDWuHAs1ycsFxULBZVX8iKIE+HdNWSX/uVT/Jd3/lH8CoBGXHlylXKcsHx8TFhGBBHEVned8YKgoCubfmK/QpfuN9QfeWTXL54nQ998COML11GCIkBpNfr/nW9we69Xz8ZSCFQQN8ZUkqBVhJpFbbpuH/3baTyXLq2h7HbLJZ9dkOYhAgtUfW60ZJzZFnIt3/gQxhraS9UfNfHvo+dnZ1TmcMzIVo//auf9HGSbERX27Q0bUldtzgnwfeirus6cI7lco6UjqvXnyEM+1zWW7duU1UVg8Fg08ech49YWrcda5qGqp6T54P+SQEChJTs7uxw4eIjT6R3vm+DveZGAGP7FA3nDEiLxFI3Nd5brDFUdU3XtkxncyyCtm16BcsHm9ZrWmu00qigf0khmM/n/Kt/5l8/H6L1YQtrnO9tOiWRShHHCabztJ3B2fUzqYRC6oDO1nzmM5/hpfe/RFVXm+eBhMHa0BYSrRTWO9qmYbFY9G3E4lHfRTKN0UFEUzfcunWbt994iyRJePH5FyDQGLt+ag9i/VgKTdf2D1+r6yWNLdna2uPencP1YwzD9W9J1p6hALzHe9BBX1WtpKJbJyRXqxUgTq2D8pngyMf07ulMmB+P6d3TYyDPCT0G8pzQYyDPCT0G8pzQYyDPCT0G8pzQYyDPCT0G8pzQYyDPCT0G8pzQYyDPCT0G8pzQYyDPCT0G8pzQYyDPCT0G8pzQYyDPCT0G8pzQYyDPCT0G8pzQYyDPCT0G8pzQ/wdNqhKCLUEOigAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x178b43a1400>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"visualize_model(model_ft)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"卷积网络作为固定的特征提取器\\n\",\n    \"----------------------------------\\n\",\n    \"\\n\",\n    \"这里, 我们固定网络中除最后一层外的所有权重. 为了固定这些参数, 我们需要设置  ``requires_grad == False`` ，然后在``backward()``中就不会计算梯度..\\n\",\n    \"\\n\",\n    \"你可以在这里阅读更多相关信息\\n\",\n    \"\\n\",\n    \"http://pytorch.org/docs/notes/autograd.html#excluding-subgraphs-from-backward\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model_conv = torchvision.models.resnet18(pretrained=True)\\n\",\n    \"for param in model_conv.parameters():\\n\",\n    \"    param.requires_grad = False\\n\",\n    \"\\n\",\n    \"# Parameters of newly constructed modules have requires_grad=True by default\\n\",\n    \"num_ftrs = model_conv.fc.in_features\\n\",\n    \"model_conv.fc = nn.Linear(num_ftrs, 2)\\n\",\n    \"\\n\",\n    \"model_conv = model_conv.to(device)\\n\",\n    \"\\n\",\n    \"criterion = nn.CrossEntropyLoss()\\n\",\n    \"\\n\",\n    \"# Observe that only parameters of final layer are being optimized as\\n\",\n    \"# opoosed to before.\\n\",\n    \"optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)\\n\",\n    \"\\n\",\n    \"# Decay LR by a factor of 0.1 every 7 epochs\\n\",\n    \"exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 训练和评估\\n\",\n    \"在使用 CPU 的情况下, 和前一个方案相比, 这将花费的时间是它的一半. 期望中, 网络的大部分是不需要计算梯度的. 前向传递依然要计算梯度.\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 0/24\\n\",\n      \"----------\\n\"\n     ]\n    },\n    {\n     \"ename\": \"KeyboardInterrupt\",\n     \"evalue\": \"\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mKeyboardInterrupt\\u001b[0m                         Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-59-029e6a48039b>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m      1\\u001b[0m \\u001b[1;31m#仅训练最后一层，时间较训练全部参数下降一半，1080TI约30min\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      2\\u001b[0m model_conv = train_model(model_conv, criterion, optimizer_conv,\\n\\u001b[1;32m----> 3\\u001b[1;33m                          exp_lr_scheduler, num_epochs=25)\\n\\u001b[0m\",\n      \"\\u001b[1;32m<ipython-input-5-77b76ce2b63b>\\u001b[0m in \\u001b[0;36mtrain_model\\u001b[1;34m(model, criterion, optimizer, scheduler, num_epochs)\\u001b[0m\\n\\u001b[0;32m     41\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m     42\\u001b[0m                 \\u001b[1;31m# statistics\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m---> 43\\u001b[1;33m                 \\u001b[0mrunning_loss\\u001b[0m \\u001b[1;33m+=\\u001b[0m \\u001b[0mloss\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m \\u001b[1;33m*\\u001b[0m \\u001b[0minputs\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0msize\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;36m0\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m     44\\u001b[0m                 \\u001b[0mrunning_corrects\\u001b[0m \\u001b[1;33m+=\\u001b[0m \\u001b[0mtorch\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0msum\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mpreds\\u001b[0m \\u001b[1;33m==\\u001b[0m \\u001b[0mlabels\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mdata\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m     45\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mKeyboardInterrupt\\u001b[0m: \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model_conv = train_model(model_conv, criterion, optimizer_conv,\\n\",\n    \"                         exp_lr_scheduler, num_epochs=25)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x23581f2cf28>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x23589dcad68>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x235826357f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x235a1958400>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x2358f18e390>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x235a19e52b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"visualize_model(model_conv)\\n\",\n    \"\\n\",\n    \"plt.ioff()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 测试集预测及提交\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"preds = []\\n\",\n    \"for i, (inputs, labels) in enumerate(testdataloaders['test']):\\n\",\n    \"    inputs = inputs.to(device)\\n\",\n    \"    labels = labels.to(device)\\n\",\n    \"\\n\",\n    \"    outputs = model_conv(inputs)\\n\",\n    \"    preds.append(torch.max(outputs, 1)[1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.save(model_conv, 'dogsvscat.pkl')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"labels=[]\\n\",\n    \"for i in range(len(preds)):\\n\",\n    \"    labels.extend(preds[i].cpu().numpy())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fileindex = []\\n\",\n    \"for i in range(12500):\\n\",\n    \"    fileindex.append(testdataloaders['test'].dataset.imgs[i][0].split('\\\\\\\\')[-1].split('.')[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"result = pd.Series(labels,index=fileindex)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"result.to_csv('submit.csv')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "src/py3.x/list2iteration.py",
    "content": "#!/usr/bin/python\n# coding:utf8\n'''\n迭代使用的是循环结构。\n递归使用的是选择结构。\n'''\nfrom __future__ import print_function\n\n# 递归求解\ndef calculate(l):\n    if len(l) <= 1:\n        return l[0]\n    value = calculate(l[1:])\n    return 10**(len(l) - 1) * l[0] + value\n\n\n# 迭代求解\ndef calculate2(l):\n    result = 0\n    while len(l) >= 1:\n        result += 10 ** (len(l)-1) * l[0]\n        l = l[1:]\n    return result\n\n\nl1 = [1, 2, 3]\nl2 = [4, 5]\nsum = 0\nresult = calculate(l1) + calculate(l2)\n# result = calculate2(l1) + calculate2(l2)\nprint(result)\n"
  },
  {
    "path": "src/script.py",
    "content": "# coding: utf-8\nimport os\nimport sys\n\n\ndef format_file(filename, str1, str2):\n    \"\"\"\n    文件内容的替换功能\n    :return:\n    \"\"\"\n    with open(filename, 'r') as f:\n        var_object = f.read()\n        if \"gitalk\" not in var_object:\n            var_object = var_object.replace(str1, str2)\n        # print(var_object)\n\n    f = open(filename, \"w\")\n    f.write(var_object)\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) == 3:\n        version, u_type = sys.argv[1], sys.argv[2]\n    else:\n        print(\"Usage: 参数个数为%s - 错误，应该改为3\" % len(sys.argv))\n        sys.exit(-1)\n\n    tag = True\n    if u_type == \"index\":\n        tag = False\n        # if version == \"home\":\n        #     filename = \"_book/index.html\"\n        # else:\n        #     filename = \"_book/docs/%s/index.html\" % version\n        # str1 = \"\"\"\n        # </head>\n        # <body>\n        # \"\"\"\n\n        # str2 = \"\"\"\n        # <script type=\"text/javascript\">\n        #     function hidden_left(){\n        #         document.getElementsByClassName(\"btn pull-left js-toolbar-action\")[0].click()\n        #     }\n        #     // window.onload = hidden_left();\n        # </script>\n        # </head>\n        # <body onload=\"hidden_left()\">\n        # \"\"\"\n    elif u_type == \"book\":\n        if version == \"home\":\n            filename = \"book.json\"\n            tag = False\n        else:\n            filename = \"docs/%s/book.json\" % version\n            str1 = \"https://github.com/apachecn/Interview/blob/master\"\n            str2 = \"https://github.com/apachecn/Interview/blob/master/docs/%s\" % version\n\n    elif u_type == \"powered\":\n        if version == \"home\":\n            filename = \"node_modules/gitbook-plugin-tbfed-pagefooter/index.js\"\n        else:\n            filename = \"docs/%s/node_modules/gitbook-plugin-tbfed-pagefooter/index.js\" % version\n        str1 = \"powered by Gitbook\"\n        str2 = \"由 ApacheCN 团队提供支持\"\n\n    elif u_type == \"gitalk\":\n        if version == \"home\":\n            filename = \"node_modules/gitbook-plugin-tbfed-pagefooter/index.js\"\n        else:\n            filename = \"docs/%s/node_modules/gitbook-plugin-tbfed-pagefooter/index.js\" % version\n        str1 = \"\"\"      var str = ' \\\\n\\\\n<footer class=\"page-footer\">' + _copy +\n        '<span class=\"footer-modification\">' +\n        _label +\n        '\\\\n{{file.mtime | date(\"' + _format +\n        '\")}}\\\\n</span></footer>'\"\"\"\n\n        str2 = \"\"\"\n      var str = '\\\\n\\\\n'+\n      '\\\\n<hr/>'+\n      '\\\\n<div align=\"center\">'+\n      '\\\\n    <p><a href=\"http://www.apachecn.org\" target=\"_blank\"><font face=\"KaiTi\" size=\"6\" color=\"red\">我们一直在努力</font></a></p>'+\n      '\\\\n    <p><a href=\"https://github.com/apachecn/Interview/\" target=\"_blank\">apachecn/Interview</a></p>'+\n      '\\\\n    <p><iframe align=\"middle\" src=\"https://ghbtns.com/github-btn.html?user=apachecn&repo=Interview&type=watch&count=true&v=2\" frameborder=\"0\" scrolling=\"0\" width=\"100px\" height=\"25px\"></iframe>'+\n      '\\\\n    <iframe align=\"middle\" src=\"https://ghbtns.com/github-btn.html?user=apachecn&repo=Interview&type=star&count=true\" frameborder=\"0\" scrolling=\"0\" width=\"100px\" height=\"25px\"></iframe>'+\n      '\\\\n    <iframe align=\"middle\" src=\"https://ghbtns.com/github-btn.html?user=apachecn&repo=Interview&type=fork&count=true\" frameborder=\"0\" scrolling=\"0\" width=\"100px\" height=\"25px\"></iframe>'+\n      '\\\\n    <a target=\"_blank\" href=\"//shang.qq.com/wpa/qunwpa?idkey=bcee938030cc9e1552deb3bd9617bbbf62d3ec1647e4b60d9cd6b6e8f78ddc03\"><img border=\"0\" src=\"http://data.apachecn.org/img/logo/ApacheCN-group.png\" alt=\"ML | ApacheCN\" title=\"ML | ApacheCN\"></a></p>'+\n      '\\\\n</div>'+\n      '\\\\n <div style=\"text-align:center;margin:0 0 10.5px;\">'+\n      '\\\\n     <script async src=\"//pagead2.googlesyndication.com/pagead/js/adsbygoogle.js\"></script>'+\n      '\\\\n     <ins class=\"adsbygoogle\"'+\n      '\\\\n         style=\"display:inline-block;width:728px;height:90px\"'+\n      '\\\\n         data-ad-client=\"ca-pub-3565452474788507\"'+\n      '\\\\n         data-ad-slot=\"2543897000\">'+\n      '\\\\n     </ins>'+\n      '\\\\n     <script>(adsbygoogle = window.adsbygoogle || []).push({});</script>'+\n      '\\\\n'+\n      '\\\\n    <script>'+\n      '\\\\n      var _hmt = _hmt || [];'+\n      '\\\\n      (function() {'+\n      '\\\\n        var hm = document.createElement(\"script\");'+\n      '\\\\n        hm.src = \"https://hm.baidu.com/hm.js?84fca651656bc67b4b2d56605b6d0852\";'+\n      '\\\\n        var s = document.getElementsByTagName(\"script\")[0]; '+\n      '\\\\n        s.parentNode.insertBefore(hm, s);'+\n      '\\\\n      })();'+\n      '\\\\n    </script>'+\n      '\\\\n'+\n      '\\\\n    <script async src=\"https://www.googletagmanager.com/gtag/js?id=UA-127082511-1\"></script>'+\n      '\\\\n    <script>'+\n      '\\\\n      window.dataLayer = window.dataLayer || [];'+\n      '\\\\n      function gtag(){dataLayer.push(arguments);}'+\n      '\\\\n      gtag(\\\\'js\\\\', new Date());'+\n      '\\\\n'+\n      '\\\\n      gtag(\\\\'config\\\\', \\\\'UA-127082511-1\\\\');'+\n      '\\\\n    </script>'+\n     '\\\\n</div>'+\n      '\\\\n'+\n      '\\\\n<meta name=\"google-site-verification\" content=\"pyo9N70ZWyh8JB43bIu633mhxesJ1IcwWCZlM3jUfFo\" />'+\n      '\\\\n<iframe src=\"https://www.bilibili.com/read/cv2710377\" style=\"display:none\"></iframe>'+ \n      '\\\\n<img src=\"http://t.cn/AiCoDHwb\" hidden=\"hidden\" />'\n\n      str += '\\\\n\\\\n'+\n      '\\\\n<div>'+\n      '\\\\n    <link rel=\"stylesheet\" href=\"https://unpkg.com/gitalk/dist/gitalk.css\">'+\n      '\\\\n    <script src=\"https://unpkg.com/gitalk/dist/gitalk.min.js\"></script>'+\n      '\\\\n    <script src=\"https://cdn.bootcss.com/blueimp-md5/2.10.0/js/md5.min.js\"></script>'+\n      '\\\\n    <div id=\"gitalk-container\"></div>'+\n      '\\\\n    <script type=\"text/javascript\">'+\n      '\\\\n        const gitalk = new Gitalk({'+\n      '\\\\n        clientID: \\\\'03eb95143c1b933f80bd\\\\','+\n      '\\\\n        clientSecret: \\\\'0fe7c3da3e15e943d1276e37732c848f8a94660a\\\\','+\n      '\\\\n        repo: \\\\'Interview\\\\','+\n      '\\\\n        owner: \\\\'apachecn\\\\','+\n      '\\\\n        admin: [\\\\'jiangzhonglian\\\\', \\\\'wizardforcel\\\\'],'+\n      '\\\\n        id: md5(location.pathname),'+\n      '\\\\n        distractionFreeMode: false'+\n      '\\\\n        })'+\n      '\\\\n        gitalk.render(\\\\'gitalk-container\\\\')'+\n      '\\\\n    </script>'+\n      '\\\\n</div>'\n\n      str += '\\\\n\\\\n<footer class=\"page-footer\">' + _copy + '<span class=\"footer-modification\">' + _label + '\\\\n{{file.mtime | date(\"' + _format + '\")}}\\\\n</span></footer>'\n        \"\"\"\n\n    # 状态为 True 就进行替换\n    if tag: format_file(filename, str1, str2)\n"
  },
  {
    "path": "src/test.py",
    "content": "\nclass Solution(object):\n    def searchInsert(self, nums, target):\n        \"\"\"\n        :type nums: List[int]\n        :type target: int\n        :rtype: int\n        \"\"\"\n        left = 0\n        right = len(nums) - 1\n        while left <= right:\n            mid = (left + right) // 2\n            print(\">>> %s: %s[%s], %s[%s], %s[%s]\" % (target, nums[left], left, nums[right], right, nums[mid], mid))\n            if nums[mid] > target:\n                right = mid - 1\n            elif nums[mid] < target:\n                left = mid + 1\n            else:\n                break\n\n        mid = mid+1 if left > mid else mid\n        print(\"结果: \", mid, left)\n\n\nif __name__ == \"__main__\":\n\n    nums = [1, 2, 4, 5, 6, 7, 8, 9, 11, 15]\n    target = -2\n    s = Solution()\n    s.searchInsert(nums, target)\n"
  },
  {
    "path": "src/其他/58.md",
    "content": "![](https://github.com/apachecn/awesome-leetcode/blob/master/src/WechatIMG438.jpeg)\n\n![](https://github.com/apachecn/awesome-leetcode/blob/master/src/WechatIMG437.jpeg)\n"
  },
  {
    "path": "src/其他/aiqiyi.md",
    "content": "```python\n\"\"\"第一题\"\"\"\nid = input()\n\nfirst, second = sum(int(i) for i in id[:3]), sum(int(i) for i in id[3:])\n\n\ndiff = second-first\nif diff == 0:\n    print(0)\nelse:\n    nums = [int(i) for i in id]\n\n    res = 0\n    if diff > 0:\n        tmp = [9-i for i in nums[:3]] + [i for i in nums[3:]]\n        while diff > 0:\n            res += 1\n            diff -= max(tmp)\n            tmp[tmp.index(max(tmp))] = 0\n        print(res)\n    else:\n        tmp = [9-i for i in nums[3:]] + [i for i in nums[:3]]\n        while diff < 0:\n            res += 1\n            diff += max(tmp)\n            tmp[tmp.index(max(tmp))] = 0\n        print(res)\n\n\n\n\"\"\"第二题\"\"\"\ntmp = input().split()\nN, M, P = int(tmp[0]), int(tmp[1]), int(tmp[2])\n\n\ntmp = input().split()\nfoods = [int(i) for i in tmp]\n# N, M, P = int(tmp[0]), int(tmp[1]), int(tmp[2])\nfor i in range(M):\n    tmp = input().split()\n    if tmp[0] == 'B':\n        foods[int(tmp[1])-1] -= 1\n    else:\n        foods[int(tmp[1])-1] += 1\n\n# print(foods)\nback_foods = sorted(foods)[::-1]\n\n# print(foods[P-1])\nprint(back_foods.index(foods[P-1])+1)\n```\n"
  },
  {
    "path": "src/其他/baicizhanxiaomi.md",
    "content": "```python\n一晚上做了好几家的，头疼，这些捞b公司贼烦\n\n我自己没有参加笔试，就是纯粹A着玩，纯属娱乐\n\n\"\"\"百词斩第一题\"\"\"\n\n\"\"\"自我实现版本\"\"\"\ndef get_time_diff(time1, time2):\n    res = 0\n    res += (int(time2[:2]) - int(time1[:2])) * 3600\n    res += (int(time2[3:5]) - int(time1[3:5])) * 60\n    res += int(time2[-2:]) - int(time1[-2:])\n    return res\n\ntime1 = input()\ntime2 = input()\n\nsec = get_time_diff(time1, time2)\nprint(int(30*sec//3600))\nprint(int(6*sec//60))\nprint(int(sec*6))\n\n\n\"\"\"工业版本\n可惜笔试不支持\n\"\"\"\n\nfrom dateutil.parser import parse\n\ndef get_time_diff(time1, time2):\n    time1 = parse(time1)\n    time2 = parse(time2)\n    return (time2-time1).total_seconds()\ntime1 = '2018-09-20/' + input()\ntime2 = '2018-09-20/' + input()\n\nsec = get_time_diff(time1, time2)\nprint(int(30*sec//3600))\nprint(int(6*sec//60))\nprint(int(sec*6))\n\n\n\"\"\"\n14:52:11\n21:41:14\n\n00:00:00\n18:00:00\n\"\"\"\n\n\"\"\"百词斩第二题\"\"\"\n\n\"\"\"百词斩第二题\"\"\"\n\nn = int(input())\nnums = [int(i) for i in input().split()]\nres = ''\n# tmp = nums[0]\ni = 0\nstart = 0\nwhile i <= n-1:\n    i += 1\n    tmp = 0\n    while i <= n-1 and nums[i] == nums[i-1]+1:\n        i += 1\n        tmp += 1\n    # print(tmp, i)\n    if tmp >= 2:\n        # print(111)\n        res += str(nums[start]) + '-' + str(nums[i-1]) + ' '\n        # start = i\n    elif tmp == 0:\n        # print(222)\n        res += str(nums[start]) + ' '\n    else:\n        # print(333)\n        # print(nums[start:i+1])\n        res += ' '.join([str(i) for i in nums[start:i]]) + ' '\n    start = i\n    # tmp = 0\nprint(res[:-1])\n\n\n\n\"\"\"\n6\n1 2 3 4 6 7\n6\n1 2 4 5 6 7\n\"\"\"\n\n\n\"\"\"不知道谁的题目\"\"\"\n\nlookup = {}\ninputs = []\nflag = True\nwhile True:\n    user_input = input()\n\n    if user_input == 'END':\n        break\n    inputs.append(user_input)\n    user_input = user_input.split('#')\n\n    n, m = user_input[0], user_input[1]\n    num = int(m, int(n))\n    lookup[num] = lookup.get(num, 0) + 1\n\nres = []\nfor i in range(len(inputs)):\n    tmp = inputs[i].split('#')\n    n, m = tmp[0], tmp[1]\n    num = int(m, int(n))\n    if lookup[num] == 1:\n        res.append(inputs[i])\n\nif not res:\n    print('None')\nelse:\n    for i in res:\n        print(i)\nprint(int('33', 4))\n\"\"\"\n10#15\n4#32\n4#33\n8#17\nEND\n\"\"\"\n\n\n“”“不知道谁的题目\n三种颜色的球\n”“”\n\nclass Solution(object):\n    res = 0\n    def cal_next(self, p, q, r, prev):\n        tmp = [p, q, r]\n        cur_max = max(tmp)\n        # 此时前面排好了且都满足，想要不相邻，数量最多的球中间的所有空格\n        # 必须小于或等于另外两个球的数量，否则说明此时排列不合法直接return\n        if cur_max - 1 > sum(tmp) - cur_max:  \n            return\n        if p == q == r == 0:\n            self.res += 1\n            return\n        if p > 0 and prev != 'p':\n            self.cal_next(p-1, q, r, 'p')\n        if q > 0 and prev != 'q':\n            self.cal_next(p, q-1, r, 'q')\n        if r > 0 and prev != 'r':\n            self.cal_next(p, q, r-1, 'r')\n\ns = Solution()\np, q, r = [int(i) for i in input().split()]\ns.cal_next(p, q, r, '')\nprint(s.res)\n\n\n```\n"
  },
  {
    "path": "src/其他/data_structure.md",
    "content": "# 我们来一起每周学习数据结构吧，可以在群里一起讨论问题\n\n# qq群号码：303677416\n\n![](https://github.com/apachecn/awesome-leetcode/blob/master/images/WechatIMG439.jpeg)\n\n"
  },
  {
    "path": "src/其他/didi.md",
    "content": "```python\n\"\"\"第一题\"\"\"\n\nlookup1 = set('qwertasdfgzxcv')\nlookup2 = set('yuiophjklbnm')\n\ndef minDistance(word1, word2):\n    if len(word1) == 0 or len(word2) == 0:\n        return max(len(word1), len(word2))\n    dp = [[0 for j in range(len(word2)+1)] for i in range(len(word1)+1)]\n    for i in range(1, len(word1)+1):\n        for j in range(1, len(word2)+1):\n            tmp_dist = 0\n            if word1[i-1] == word2[j-1]:\n                tmp_dist == 0\n            elif (word1[i-1] in lookup1 and word2[j-1] in lookup1) or (word1[i-1] in lookup2 and word2[j-1] in lookup2):\n                tmp_dist = 1\n            else:\n                tmp_dist = 2\n            dp[i][j] = min(dp[i-1][j]+3, dp[i][j-1]+3, dp[i-1][j-1]+tmp_dist)\n    # print(dp)\n    return dp[-1][-1]\n\n\"\"\"\nslep slap sleep step shoe shop snap slep\n\"\"\"\n\nstrs = input().split(' ')\n# print(strs)\ndists = [0]\nfor i in range(1, len(strs)):\n    dists.append((minDistance(strs[0], strs[i])))\n# print(dists)\ntmp = [0] * len(dists)\nfor i in range(1, len(dists)):\n    tmp[i] = [dists[i], strs[i]]\ntmp = tmp[1:]\n\ntmp = sorted(tmp, key=lambda x:x[0])\n# print(tmp)\nres = tmp[0][1]+' ' + tmp[1][1]+' '+tmp[2][1]\nprint(res)\n\n\n\n\"\"\"第二题\"\"\" PS: 这题感谢算法群里的kkk大佬指点我\n\nclass Solution(object):\n    res = 0\n    def cal_next(self, p, q, r, prev):\n        tmp = [p, q, r]\n        cur_max = max(tmp)\n        # 此时前面排好了且都满足，想要不相邻，数量最多的球中间的所有空格\n        # 必须小于或等于另外两个球的数量，否则说明此时排列不合法直接return\n        if cur_max - 1 > sum(tmp) - cur_max:  \n            return\n        if p == q == r == 0:\n            self.res += 1\n            return\n        if p > 0 and prev != 'p':\n            self.cal_next(p-1, q, r, 'p')\n        if q > 0 and prev != 'q':\n            self.cal_next(p, q-1, r, 'q')\n        if r > 0 and prev != 'r':\n            self.cal_next(p, q, r-1, 'r')\n\ns = Solution()\np, q, r = [int(i) for i in input().split()]\ns.cal_next(p, q, r, '')\nprint(s.res)\n```\n题目图片见下方：\n### 第一题\n![](https://github.com/apachecn/awesome-leetcode/blob/master/src/51CFCB8475F4ED5B1CAD5F23CF96887A.jpg)\n![](https://github.com/apachecn/awesome-leetcode/blob/master/src/B9A582497F833DDE4E93FA5BAA0BA4EB.jpg)\n### 第二题\n![](https://github.com/apachecn/awesome-leetcode/blob/master/src/5C3A0153C03FD214FE68B002903E2135.jpg)\n\n"
  },
  {
    "path": "src/其他/glodon.md",
    "content": "```python\n\n\"\"\"第一题\"\"\"\n\n​​​​​​​def f(n):\n    res = 0\n    for i in range(1, n+1, 2):\n        res += i\n    for i in range(2, n+1, 2):\n        res -= i\n    return res\n\n\n\"\"\"第二题\"\"\"\n\n​​​​​​​res = []\nnums = ['9','8','7','6','5','4','3','2','1']\n\n\ndef convert(formula):\n    if not formula or len(formula) == 0:\n        return 0\n    tmp = ''\n    for i in range(len(formula)):\n        if formula[i] == '+' formula[i] != '+' and formula[i] != '-':\n            return int(tmp) + valid(formula[i+1:]) \n        elif formula[i] == '-':\n            return int(tmp) - valid(formula[i+1:])\n        else:\n            tmp += formula[i]\n\ndef valid(formula):\n    if convert(formula) == 100:\n        return True\n    return False\n        \n            \ndef cal(formu, idx):\n    if idx == len(nums) - 1:\n        formula = ''.join(formu)\n        if valid(formula):\n            res.append(formula)\n    for i in range(idx, len(nums)-1):\n        cal(formu, i+1)\n        cal(formu[:i] + [formu[i]+'+'] + formu[i+1:], i+1)\n        cal(formu[:i] + [formu[i]+'-'] + formu[i+1:], i+1)\n\n\ncal(nums, 0)\nprint('Results of formulas below all equal to 100')\nfor i in res:\n    print(i)\n\n\"\"\"第三题\"\"\"\n\n​​​​​​​假设我们开始排列是1,2,3,4,5\n然后首先我们要让第一个位置变成2，3，4或者5才满足条件，而在其中我们又会遇到原位置没有变化的情况，则我们知道每次变换2个位置的时候总有重复，因为两边计算出的所有情况中总有一半是不满足的，并且是对于所有位置不同的情况下，那么我们不妨从第一个位置开始，则公式为   ，其中n为小朋友的个数   \n\n开始编码：\nfrom math import factorial\ndef cal(n):\n    return (n-1) * factorial(n-1) // 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                         \n\n\n\n\n\"\"\"第四题\"\"\"\n\n​​​​​​​import sys\ndef maxSum(nums, window_len):\n    res = -sys.maxsize\n    for i in range(len(nums)-window_len):\n        res = max(res, sum(nums[i:i+k]))\n    return res\n        \n\n\n\n\"\"\"第五题\"\"\"\n\nclass ListNode(object):\n    def __init__(self, val, next, sib):\n        self.val = val\n        self.next = next\n        self.sib = sib\n\nclass Clone(object)\n    def cloneComplicateListNode(self, head):\n        if not head:\n            return head\n        dummy = cur = ListNode(head.val, None, None)\n        while head:\n            cur.next = self.cloneComplicateListNode(head.next)\n            cur.sib = self.cloneComplicateListNode(head.sib)\n            head = head.next\n            cur = cur.next\n        return dummy\n        \n```\n"
  },
  {
    "path": "src/其他/huaweiliuxuesheng.md",
    "content": "\"\"\"ip 地址转换\"\"\"\n```python\ndb_num = int(input())\ndef dec2addr(dec):\n    res = []\n    for i in range(3, -1, -1):\n        if dec // (256 ** i) > 256 ** (4-i):\n            return '0.0.0.0'\n        res.append(str(dec // (256 ** i) % 256))\n    return '.'.join(res)\n\nprint(dec2addr(db_num))\n```\n\n\n\n\"\"\"页码问题\"\"\"\n```python\ndef pageCount(n):\n    res = [0] * 10\n    for i in range(10):\n        page, base, cnt = n, 0, 0\n        while (page // (10 ** (base)) > 0):\n            tmp = (page % (10 ** (base + 1))) // (10 ** base)\n            left = page // (10 ** (base + 1))\n            right = page % (10 ** (base))\n            if (i == 0):\n                left -= 1\n            if (i > tmp):\n                cnt += left * (10 ** base)\n            elif (i < tmp):\n                cnt += (left + 1) * (10 ** base)\n            else:\n                cnt += (left * (10 ** base) + right + 1)\n            base += 1\n        res[i] = cnt\n    return ' '.join(str(i) for i in res[::-1])\n\n\nn = int(input())\nif 1 <= n <= 10 ** 9:\n    print(pageCount(n))\nelse:\n    print(-1)\n```\n\n\n\"\"\"约瑟夫问题\"\"\"\n```python\nn, m = map(int, input().split(','))\n\ndef joseph(people, cnt):\n    queue = list(range(1, people + 1))\n    death = (cnt - 1) % len(queue)\n    for i in range(people - 1):\n        del queue[death]\n        death = (death + cnt - 1) % len(queue)\n    return queue[0]\n\nprint(joseph(n, m))\n```\n"
  },
  {
    "path": "src/其他/indeed_tokyo.md",
    "content": "```python\n\"\"\"第一题\"\"\"\ntmp = input().split()\nn, q = int(tmp[0]), int(tmp[1])\nnums = [int(i) for i in input().split()]\nidx_ms = []\nfor i in range(q):\n    tmp = input().split()\n    idx, m = int(tmp[0]), int(tmp[1])\n    idx_ms.append([idx, m])\nsums = sum([i for i in nums if i & 1 == 0])\nres = [sums] * (q+1)\nfor i in range(q):\n    flag = nums[idx_ms[i][0]-1] & 1 == 0\n\n    if flag and idx_ms[i][1] + nums[idx_ms[i][0]-1] & 1 == 0:\n        res[i+1] = res[i] + idx_ms[i][1]\n    elif flag and idx_ms[i][1] + nums[idx_ms[i][0]-1] & 1 != 0:\n        res[i+1] = res[i] - nums[idx_ms[i][0] - 1]\n    elif not flag and idx_ms[i][1] + nums[idx_ms[i][0]-1] & 1 == 0:\n        res[i + 1] = res[i] + idx_ms[i][1] + nums[idx_ms[i][0] - 1]\n\n    else:\n        res[i+1] = res[i]\n\n    nums[idx_ms[i][0] - 1] += idx_ms[i][1]\n\n# print(res[1:])\n\nfor i in range(q):\n    print(res[i+1])\n\n\n\n\n\n\"\"\"第二题\"\"\"\nimport sys\nN = int(input())\nnums = [-sys.maxsize] + [int(i) for i in input().split()]\nres = 0\nflag = 1\nfor i in range(len(nums)):\n    if nums[i] >= nums[i-1]:\n        continue\n    elif nums[i] <= 0 and -nums[i] >= nums[i-1]:\n        res += 1\n        nums[i] = -nums[i]\n    elif nums[i] <= 0 and -nums[i] < nums[i-1]:\n        flag = 0\n        print(-1)\n        break\n    elif nums[i] < nums[i-1] and nums[i] > 0:\n        flag = 0\n        print(-1)\n        break\nif flag:\n    print(res)\n    \n    \n    \n\n\"\"\"第三题\"\"\"\nclass Solution():\n    def __init__(self):\n        self.res = 0\n        self.lst = []\n    def sol(self):\n        tmp = input().split()\n        row, col = int(tmp[0]), int(tmp[1])\n\n        grid = []\n        for i in range(row):\n            grid.append([i for i in input()])\n        def bfs_paths(grid, start, goal):\n            if start[0] == goal[0] and start[1] == goal[1]:\n                self.res += 1\n            if start[0] == row-1 and start[1] == col-1:\n                return\n            # print(start, goal)\n            if 0 <= start[0] + 1 < row and grid[start[0]+1][start[1]] == '.':\n                bfs_paths(grid, [start[0]+1,start[1]], goal)\n            if 0 <= start[1] + 1 < col and grid[start[0]][start[1]+1] == '.':\n                bfs_paths(grid, [start[0],start[1]+1], goal)\n            # return res\n        n = int(input())\n        for i in range(n):\n            tmp = input().split()\n            goal1, goal2 = int(tmp[0]), int(tmp[1])\n            # print(goal1, goal2)\n\n\n            bfs_paths(grid, [0,0], [goal1-1,goal2-1])\n            # print([goal1-1,goal2-1])\n            self.lst.append(self.res % (pow(10, 9) + 7))\n            self.res = 0\n\n\ns = Solution()\ns.sol()\nfor i in s.lst:\n    print(i)\n    \n    \n\n\"\"\"第四题\"\"\"\ntmp = input().split()\nn, m = int(tmp[0]), int(tmp[1])\nrequi = []\nfor i in range(m):\n    nums = [int(i) for i in input().split()]\n    requi.append(nums)\n\nkeys =set([1])\nrooms = set([1])\n# flag = True\nwhile True:\n    flag1 = False\n    for i in requi:\n\n        if i[0] in rooms and i[1] not in rooms:\n            if i[2] in keys:\n                flag1 = True\n                rooms.add(i[1])\n                keys.add(i[1])\n    if not flag1:\n        break\n\nprint(len(rooms))\n```\n"
  },
  {
    "path": "src/其他/kuaishou.md",
    "content": "```python\n\"\"\"第一题\"\"\"\n\n\ntmp = input().split()\nnumerator = int(tmp[0])\ndenominator = int(tmp[1])\n\ndef fractionToDecimal(numerator, denominator):\n    if numerator == 0:\n        return '0'\n    res = ''\n    if numerator * denominator < 0:\n        res += '-'\n    numerator, denominator = abs(numerator), abs(denominator)\n    res += str(numerator // denominator)\n    if numerator % denominator == 0:\n        return res\n    res += '.'\n    r = numerator % denominator\n    m = {}\n    # print(res)\n    while r:\n        if r in m:\n            res = res[:m[r]] + '(' + res[m[r]:] + ')'\n            break\n        m[r] = len(res)\n        r *= 10\n        res += str(r // denominator)\n        r %= denominator\n\n    return res\n\n\nres = fractionToDecimal(numerator, denominator)\n# print(res)\nif '(' not in res:\n    if '.' not in res:\n        print(int(res))\n    else:\n        print(res)\nelse:\n    print(res)\n\n\n\n\n\n\n\n\"\"\"第二题\"\"\"\n\n\ntmp = input().split(',')\nif not tmp:\n    print(0)\nelse:\n    costs = [int(i) for i in tmp]\n\n    if len(costs) == 1:\n        print(costs[-1])\n    elif len(costs) == 2:\n        print(costs[-1])\n    else:\n        dp = [0] * len(costs)\n        dp[0] = costs[0]\n        dp[1] = costs[1]\n\n\n        for i in range(2, len(dp)):\n            dp[i] = min(dp[i-1], dp[i-2]) + costs[i]\n\n        print(min(dp[-3], dp[-2]) + costs[-1])\n\n\n\n\n\n\"\"\"第三题\"\"\"\ndef max_sub_array(nums):\n    n = len(nums)\n    maxSum = [nums[0] for i in range(n)]\n    for i in range(1, n):\n        maxSum[i] = max(maxSum[i - 1] + nums[i], nums[i])\n    return (maxSum.index(max(maxSum)), max(maxSum))\ndef min_sub_array(nums):\n    n = len(nums)\n    minSum = [nums[0] for i in range(n)]\n    for i in range(1, n):\n        minSum[i] = min(minSum[i - 1] + nums[i], nums[i])\n    return (minSum.index(min(minSum)), min(minSum))\n\ntmp = input().split()\nN, M = int(tmp[0]), int(tmp[1])\ntmp = input().split(' ')\nlevels = [int(i) for i in tmp]\nbackup_levels = [i for i in levels]\nidxes = []\nres = 0\nk = 0\nfor i in range(M):\n    idx, cur_max = max_sub_array(levels)\n    if cur_max >= 0:\n        cur_sum = 0\n        left_idx = 0\n        for j in range(idx, -1, -1):\n            cur_sum += levels[j]\n            if cur_sum == cur_max:\n                left_idx = j\n                idxes.append([left_idx, idx])\n                break\n        levels = levels[:left_idx] + levels[idx+1:]\n        # print(cur_max)\n        res += cur_max\n        # print(res)\n    else:\n\n        # tmp = min_sub_array(backup_levels[idxes[i][0]:idxes[i][1] + 1])[1]\n        idx, cur_min = min_sub_array(backup_levels[idxes[k][0]:idxes[k][1] + 1])\n        if cur_min <= 0:\n            cur_sum = 0\n            left_idx = 0\n            for j in range(idx, -1, -1):\n                cur_sum += backup_levels[j]\n                if cur_sum == cur_max:\n                    left_idx = j\n                    # idxes.append([left_idx, idx])\n                    break\n            # print(left_idx, idx)\n        k += 1\n        res += -cur_min if cur_min <= 0 else 0\n\n# print(res)\n# print(backup_levels)\n# print(idxes)\n\n\nprint(res)\n# print()\n# print(min_sub_array(backup_levels[0:5]))\n```\n"
  },
  {
    "path": "src/其他/liulishuo.md",
    "content": "```python\n\n\"\"\"第一题\"\"\"\nn = int(input())\nnums = []\nfor i in range(n):\n    nums.append(int(input()))\n\nmax_sum, max_end = nums[0], nums[0]\nfor i in range(1, len(nums)):\n    max_end = max(max_end + nums[i], nums[i])\n    max_sum = max(max_sum, max_end)\nprint(max_sum)\n\n\n\"\"\"问答题\n\nLet's say the egg1 and egg2\n\nFirstly, use egg1 from 2nd floor and drop it, if egg1 break, then use egg2 to check 1th floor, if egg2 doesn't break, then the highest floor we need is 1th, otherwise 2nd floor is what we want. If from neither floor two eggs don't break, we can do this from 4th and 3rd, 6th and 5th, 8th and 7th,,,,,,100th and 99th.\n\nFrom this method, we can easily get this problem reduced by 2 and totally time complexity will be O(lgn) if in programming.\n\n\"\"\"\n\n```\n"
  },
  {
    "path": "src/其他/pdd.md",
    "content": "```python\n\"\"\"第一题\"\"\"\n\nmat = [[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n       [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n       [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]]\n\n\ndef maxOne(mat):\n    if not mat or len(mat) == 0:\n        return []\n\n    row = len(mat)\n    col = len(mat[0]) if row else 0\n    i, j = -1, -1\n    for idx in range(row):\n        if mat[idx][-1] == 1:\n            i, j = idx, col - 1\n            break\n    if i == -1:\n        return []\n    fin_max = 0\n    while j >= 0 and mat[i][j] == 1:\n        j -= 1\n        fin_max += 1\n    res = [[i+1, fin_max]]\n    tmp_j = j + 1\n    while i + 1 < row:\n        i += 1\n        j = tmp_j\n        if mat[i][j] == 1:\n            cur_max = fin_max - 1\n            while j >= 0 and mat[i][j] == 1:\n                j -= 1\n                cur_max += 1\n            if cur_max > fin_max:\n                res = [[i+1, cur_max]]\n                fin_max = cur_max\n            elif cur_max == fin_max:\n                res.append([i+1, cur_max])\n    return res\n\nprint(maxOne(mat))\n\n\n\"\"\"第三题\"\"\"\n\nclass Solution(object):\n    def __init__(self):\n        self.stack = []\n        self.res = 0\n    def dfs(self, arr, x, y, c):\n        m = len(arr)\n        n = len(arr[0]) if m else 0\n        if x - 1 >= 0 and y >= 0 and arr[x-1][y] == 'x':\n            self.stack.append((x-1)*100+y)\n            arr[x-1][y] = '#'\n            self.res += 1\n        if x + 1 < m and y >= 0 and arr[x+1][y] == 'x':\n            self.stack.append((x+1)*100+y)\n            arr[x+1][y] = '#'\n            self.res += 1\n        if x >= 0 and y - 1 >= 0 and arr[x][y-1] == 'x':\n            self.stack.append(x*100+y-1)\n            arr[x][y-1] = '#'\n            self.res += 1\n        if x >= 0 and y + 1 < n and arr[x][y+1] == 'x':\n            self.stack.append(x*100+y+1)\n            arr[x][y+1] = '#'\n            self.res += 1\n        while self.stack:\n            tmp = self.stack.pop()\n            self.dfs(arr, tmp //100, tmp % 100, 'x')\n        return self.res\n\nif __name__ == '__main__':\n    s = Solution()\n    arr = [['x',' ',' ', ' ',' '],['x','x','x',' ','x'],['x','x','x','x','x']]\n    print(s.dfs(arr, 0, 0, 'x'))\n\n\n\n\"\"\"第四题\"\"\"\n\n\nimport heapq\nimport sys\ndef solution(nums):\n    iters = [iter(l) for l in nums]\n    heap = [(next(it), i) for i, it in enumerate(iters)]\n    heapq.heapify(heap)\n\n    lo, hi = 0, sys.maxsize\n    r = max(heap)[0]\n    while True:\n        l, i = heap[0]\n        if r - l < hi - lo:\n            lo, hi = l, r\n        nxt = next(iters[i], None)\n        if nxt is None:\n            return [lo, hi]\n        r = max(r, nxt)\n        heapq.heappushpop(heap, (nxt, i))\n\nnums = [[1,3,5],[4,8],[2,5]]\nprint(solution(nums))\n\n```\n"
  },
  {
    "path": "src/其他/qqchangE_problem.md",
    "content": "```python\ndef canPartition(nums):\n    def dfs(nums, target, num):\n        n = len(nums)\n        for i in range(n):\n            B = nums[:i] + nums[i + 1:]\n            if num + nums[i] == target:\n                return True\n            elif num + nums[i] < target:\n                if dfs(B, target, num + nums[i]):\n                    return True\n            elif num == 0:  # 有一个数比sum/2还大，直接返回False\n                return False\n        return False\n\n    total = sum(nums)\n\n    if total % 2 != 0:\n        return False\n\n    target = total // 2\n\n    nums.sort(reverse=True)  # 逆序排序，先从大的开始判断，速度会更快\n\n    res = dfs(nums, target, 0)\n    return res\n\n\nprint(canPartition([3, 3, 3, 4, 5]))\nprint(canPartition([1,1,2]))\nprint(canPartition([1,2,3,4]))\nprint(canPartition([2,3]))\nprint(canPartition([2,2,2,2,6]))\nprint(canPartition([1,2,2,2,2,7]))\n\n\noutput:\nTrue\nTrue\nTrue\nFalse\nFalse\nTrue\n```\n"
  },
  {
    "path": "src/其他/shopee.md",
    "content": "```python\n\"\"\"第一题千分位\"\"\"\n\nn = input()[::-1]\nif not n or len(n) == 0:\n    print('')\nelse:\n    if n[-1] == '-':\n        n = n[:-1]\n        tmp = []\n        for i in range(0, len(n), 3):\n            tmp.append(n[i:i+3])\n        # print(tmp)\n        print('-'+','.join(i[::-1] for i in tmp[::-1]))\n    else:\n        tmp = []\n        for i in range(0, len(n), 3):\n            tmp.append(n[i:i + 3])\n\n        print(','.join(i[::-1] for i in tmp[::-1]))\n\n\n\n\n\"\"\"第二题通配符\"\"\"\np = input()\ns = input()\n\n\ndef isMatch(s, p):\n    def helper(s, i, p, j):\n        if j == -1:\n            return i == -1\n        if i == -1:\n            if p[j] == '*' or p[j] == '#':\n                return helper(s, i, p, j - 1)\n            return False\n        if p[j] == '*':\n            return helper(s, i - 1, p, j) or helper(s, i, p, j-1)\n        if p[j] == '#':\n            return helper(s, i - 1, p, j-1) or helper(s, i, p, j - 1)\n        if p[j] == '?' or p[j] == s[i]:\n            return helper(s, i - 1, p, j - 1)\n        return False\n\n    return helper(s, len(s) - 1, p, len(p) - 1)\n# print(1 if isMatch(s, p) else 0)\n\n\ntmp = [['a?c', 'abc'], ['a?c', 'ac'], ['a#c', 'ac'], ['a#c', 'abc'], ['a#c', 'abbc'], ['a*c', 'ac'],\n       ['a*c', 'abc'], ['a*c', 'abbc'], ['a*c', 'abd'], ['a?c#e*f', 'abcdef'], ['a?c#e*f', 'acdef']]\n# for i in tmp:\n#     p, s = i[0], i[1]\n#     print(1 if isMatch(s, p) else 0)\n\nprint(1 if isMatch(s, p) else 0)\n\"\"\"\n输出应该为：\n1\n0\n1\n1\n0\n1\n1\n1\n0\n1\n0\n\"\"\"\n```\n"
  },
  {
    "path": "src/其他/sohuchangyouweipinhui.md",
    "content": "```python\n\"\"\"搜狐畅游第一题\"\"\"\nnums = [int(i) for i in input().split()]\nprint(int(sum(nums) - (len(nums)-1)*(len(nums)-2)/2))\n\n\n\"\"\"唯品会第一题\"\"\"\nimport heapq\n[k, n] = [int(i) for i in input().split()]\ntopk = []\nfor i in range(n):\n    nums = [int(i) for i in input().split()]\n    for num in nums:\n        heapq.heappush(topk, num)\nres = 0\nfor i in range(k):\n    res = heapq.heappop(topk)\nprint(res)\n\n\n\"\"\"唯品会第二题\"\"\"\nprint(bin(sum([int(i, 2) for i in input().split()]))[2:])\n```\n"
  },
  {
    "path": "src/其他/tecent.md",
    "content": "```python\n\n\n\n# k = int(input())\n# a = input()\n# b = input()\n# set_a = set()\n# for i in range(0, len(a)-k+1):\n#     set_a.add(a[i:i+k])\n# res = 0\n# for i in range(0, len(b)-k+1):\n#     if b[i:i+k] in set_a:\n#         res += 1\n# print(res)\n\"\"\"\n2\nabab\nababab\n\"\"\"\n\n\"\"\"\n7 14\n\"\"\"\n\nfrom math import sqrt\nnums = [int(i) for i in input().split()]\nx, y = nums[0], nums[1]\n\n# n = 1\n# while (n+1)*n/2 != sum(nums):\n#     n += 1\n\nn = int((sqrt(8*sum(nums)+1)-1)/2)\n\npermu = [i+1 for i in range(n)]\n\nres = []\ndef exist(permu, x, path):\n    if not permu and x != 0:\n        return (False, -1)\n\n    if x == 0 or x in permu:\n        res.append(path+1)\n        return (True, path + 1)\n    if exist(permu[1:], x-permu[0], path+1)[0]:\n        res.append(exist(permu[1:], x-permu[0], path+1)[1])\n        return (True, exist(permu[1:], x-permu[0], path+1)[1])\n    if exist(permu[1:], x, path+1)[0]:\n        res.append(exist(permu[1:], x, path+1)[1])\n        return (True, exist(permu[1:], x, path+1)[1])\n\nexist(permu, x, 0)\nprint(res)\nprint(min(res) if res else -1)\n\n\"\"\"\n2 3 3\n\"\"\"\n# res = 0\n# def tri(a, b, c):\n#     if a + b > c and a + c > b and b + c > a:\n#         return True\n#     return False\n#\n# nums = [int(i) for i in input().split()]\n# res = 0\n# for i in range(1, nums[0]+1):\n#     for j in range(i+1, nums[1]+1):\n#         for k in range(j, nums[2]+1):\n#             if tri(i, j, k):\n#                 res += 3\n#\n# print(res)\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n# from math import sqrt\n# nums = [int(i) for i in input().split()]\n# x, y = nums[0], nums[1]\n#\n# n = 1\n# while (n+1)*n/2 != sum(nums):\n#     n += 1\n#\n# n = int((sqrt(8*sum(nums)+1)-1)/2)\n#\n# permu = [i+1 for i in range(n)]\n# res = 0\n# while x > 0:\n#     x -= permu[-1]\n#     permu = permu[:-1]\n#     res += 1\n# print(res)\n```\n"
  },
  {
    "path": "src/其他/times.md",
    "content": "http://bigocheatsheet.com/\n\n\nhttps://algs4.cs.princeton.edu/cheatsheet/\n\nhttps://wiki.python.org/moin/TimeComplexity\n"
  },
  {
    "path": "src/其他/vmware.md",
    "content": "```python\n\"\"\"第一题： 线段数和三角形数\"\"\"\nn = int(input())\n\nif n == 1:\n    print(0, 0)\nelif n == 2:\n    print(1, 0)\nelif n == 3:\n    print(3, 1)\nelse:\n    # print(int(6+(n-4)/2*(2+(n-4)*2)), int(6+(n-4)/2*(2+(n-4)*2))-2)\n    print((n-3)*3+3, (n-3)*3+1)\n\n\n\n\"\"\"第二题：最小交换次数\"\"\"\nn = int(input())\ntmp = input().split()\nnums = [int(i) for i in tmp]\nlength = len(nums)\ndef sort1(nums):\n    res = 0\n    for i in range(length):\n        swapped = False\n        for j in range(length-1):\n            if nums[j] > nums[j+1]:\n                swapped = True\n                nums[j], nums[j+1] = nums[j+1], nums[j]\n                res += 1\n        if not swapped:\n            break\n    return res\ndef sort2(nums):\n    res = 0\n    for i in range(length):\n        swapped = False\n        for j in range(length-1):\n            if nums[j] < nums[j+1]:\n                swapped = True\n                nums[j], nums[j+1] = nums[j+1], nums[j]\n                res += 1\n        if not swapped:\n            break\n    return res\n\nprint(min(sort1(nums), sort2(nums)))\n\n\n\n\n\"\"\"第三题： 搬家\"\"\"\ntmp = input().split()\nN, W = int(tmp[0]), int(tmp[1])\nsums = 0\nfor i in range(N):\n    sums += int(input())\nif sums == 0:\n    print(0)\nelse:\n    if sums <= W:\n        print(1)\n    elif sums == N * W:\n        print(N)\n    else:\n        print(sums//W + 1)\n```\n"
  },
  {
    "path": "src/其他/wangyihuyudierti.java",
    "content": "import java.util.*;\n\npublic class Main {\n    public static void main(String[] args) {\n        Scanner sc = new Scanner(System.in);\n\n        int n = sc.nextInt();\n\n        for (int i=0; i<=n; i++){\n            String s = sc.nextLine();\n            // System.out.println(s);\n            String[] xynum = s.split(\" \");\n            // System.out.println(xynum[0]);\n            int x, y;\n            try{\n                x = Integer.parseInt(xynum[0]);\n            }catch(NumberFormatException ex){ // handle your exception\n                continue;\n            }\n            try{\n                y = Integer.valueOf(xynum[1]);\n            }catch(NumberFormatException ex){ // handle your exception\n                continue;\n            }\n            // System.out.println((Helper(xynum[2].substring(0,2),x)));\n            for (int j=1; j<xynum[2].length(); j++){\n                if (Helper(xynum[2].substring(0,j),x) == Helper(xynum[2].substring(j,xynum[2].length()),y)){\n                    System.out.println(Helper(xynum[2].substring(0,j),x));\n                }\n            }\n\n            // System.out.println(Helper(\"111\", 2));\n\n            // System.out.println(s);\n            // System.out.println(res);\n        }\n    }\n\n    private static int Helper(String s, int k) {\n        int res = 0;\n        for (int i=s.length()-1;i>=0;i--){\n            char chr = s.charAt(i);\n            int tmp;\n            if (chr == 'A') {\n                tmp = 10;\n            }else if(chr == 'B') {\n                tmp = 11;\n            }else if(chr == 'C') {\n                tmp = 12;\n            }else if(chr == 'D') {\n                tmp = 13;\n            }else if(chr == 'E') {\n                tmp = 14;\n            }else if(chr == 'F') {\n                tmp = 15;\n            }\n            else{\n                tmp = Integer.parseInt(Character.toString(chr));\n            }\n\n            // System.out.println(\"tmp\"+tmp);\n            res += tmp * Math.pow(k, s.length()-1-i);\n            // System.out.println(\"res\"+res);\n        }\n        return res;\n    }\n}\n/*\n1\nABCDE\n */\n"
  },
  {
    "path": "src/其他/xunlei.md",
    "content": "```python\n\"\"\"第一题\"\"\"\nN = int(input())\n\n\ndef gcd(big, small):\n    if big < small:\n        small, big = big, small\n    remainder = big % small\n    if remainder == 0:\n        return small\n    else:\n         return gcd(small,remainder)\n\nres = 0\nfrom math import sqrt\nfor c in range(1, N+1):\n    for a in range(1, c):\n        for b in range(max(c-a+1,a), c):\n            if a * a + b * b == c * c:\n                if gcd(a, b) == 1 and gcd(b, c) == 1 and gcd(a, c) == 1:\n                    res += 1\n                    # print(a,b,c)\n\nprint(res)\n\n\n\"\"\"第二题\"\"\"\ntmp = input().split()\npositive, negative = int(tmp[0]), int(tmp[1])\n# print(positive, negative)\n\nidx = 0\nfor i in range(6, -1, -1):\n    if i*positive+(7-i)*negative < 0:\n        idx = i\n        break\n# print(idx)\n\n\nres = (negative*(7-idx)+positive*idx) * 2\n# print(res)\nif idx >= 3:\n    res += 3 * positive\nelse:\n    res += idx * positive + (3 - idx) * negative\n\nprint(res)\n```\n"
  },
  {
    "path": "src/其他/zhaoyincreditcard.md",
    "content": "```python\n\"\"\"第一题\"\"\"\ngi = [int(i) for i in input().split()]\nsj = [int(i) for i in input().split()]\nres = 0\nfor i in sj:\n    tmp = [j for j in gi if j <= i]\n    if tmp:\n        res += 1\n        gi.remove(max(tmp))\nprint(res)\n\n\n\"\"\"第二题\"\"\"\nn = int(input())\nif n == 1:\n    print(1)\nelif n == 2:\n    print(2)\nelse:\n    dp = [0] * n\n    dp[0] = 1\n    dp[1] = 2\n    for i in range(2, n):\n        dp[i] = dp[i-1] + dp[i-2]\n    print(dp[-1])\n\n\n\"\"\"第三题\"\"\"\ninvalid = ['3', '4', '7']\nnochange = ['0', '1', '8']\ndef good(n):\n    n = str(n)\n    for i in invalid:\n        if i in n:\n            return False\n    res = ''\n    for i in n:\n        if i in nochange:\n            res += i\n        if i == '2':\n            res += '5'\n        elif i == '5':\n            res += '2'\n        elif i == '6':\n            res += '9'\n        elif i == '9':\n            res += '6'\n    return res != n\n\nn = int(input())\nres = 0\nfor i in range(n+1):\n    if good(i):\n        res += 1\nprint(res)\n```\n"
  },
  {
    "path": "src/其他/zhaoyinwangluokeji.java",
    "content": "import java.util.Scanner;\n\npublic class Main {\n    public static void main(String[] args) {\n        Scanner sc = new Scanner(System.in);\n\n        int n = sc.nextInt();\n\n        Helper(n);\n    }\n\n    private static void Helper(int n) {\n        int count = 90;\n        int start = 1;\n        String res = \"\";\n        // System.out.print((count * 1.0 - (count-1)*count/2.0)/count);\n        while ((count * 1.0 - (count-1)*count/2.0)/count < start*1.0){\n            for (int i = 0; i <= n / count; i++) {\n                if (count * i + (count-1) * count /2.0 == n) {\n                    System.out.print(\"[\");\n                    res += \"[\";\n                    for (int j =i; j <= i+count-1; j++){\n                        if (j==i+count-1){\n                            System.out.println(j+\"]\");\n                            res += Integer.toString(j) + \"]\";\n                            break;\n                        }\n                        System.out.print(j+\", \");\n                        res += Integer.toString(j) + \", \";\n\n                    }\n                    // System.out.print(\"]\");\n                    // System.out.println(\"\");\n                }\n            }\n            count -= 1;\n        }\n        res = new StringBuffer(res).reverse().toString();\n        // System.out.print(res);\n\n    }\n}\n"
  },
  {
    "path": "update.sh",
    "content": "git add -A\ngit commit -am \"$(date \"+%Y-%m-%d %H:%M:%S\")\"\ngit push"
  }
]