finding_donors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习纳米学位\n",
    "## 监督学习\n",
    "## 项目2: 为*CharityML*寻找捐献者"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "欢迎来到机器学习工程师纳米学位的第二个项目！在此文件中，有些示例代码已经提供给你，但你还需要实现更多的功能让项目成功运行。除非有明确要求，你无须修改任何已给出的代码。以**'练习'**开始的标题表示接下来的代码部分中有你必须要实现的功能。每一部分都会有详细的指导，需要实现的部分也会在注释中以'TODO'标出。请仔细阅读所有的提示！\n",
    "\n",
    "除了实现代码外，你还必须回答一些与项目和你的实现有关的问题。每一个需要你回答的问题都会以**'问题 X'**为标题。请仔细阅读每个问题，并且在问题后的**'回答'**文字框中写出完整的答案。我们将根据你对问题的回答和撰写代码所实现的功能来对你提交的项目进行评分。\n",
    ">**提示：**Code 和 Markdown 区域可通过**Shift + Enter**快捷键运行。此外，Markdown可以通过双击进入编辑模式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 开始\n",
    "\n",
    "在这个项目中，你将使用1994年美国人口普查收集的数据，选用几个监督学习算法以准确地建模被调查者的收入。然后，你将根据初步结果从中选择出最佳的候选算法，并进一步优化该算法以最好地建模这些数据。你的目标是建立一个能够准确地预测被调查者年收入是否超过50000美元的模型。这种类型的任务会出现在那些依赖于捐款而存在的非营利性组织。了解人群的收入情况可以帮助一个非营利性的机构更好地了解他们要多大的捐赠，或是否他们应该接触这些人。虽然我们很难直接从公开的资源中推断出一个人的一般收入阶层，但是我们可以（也正是我们将要做的）从其他的一些公开的可获得的资源中获得一些特征从而推断出该值。\n",
    "\n",
    "这个项目的数据集来自[UCI机器学习知识库](https://archive.ics.uci.edu/ml/datasets/Census+Income)。这个数据集是由Ron Kohavi和Barry Becker在发表文章_\"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid\"_之后捐赠的，你可以在Ron Kohavi提供的[在线版本](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf)中找到这个文章。我们在这里探索的数据集相比于原有的数据集有一些小小的改变，比如说移除了特征`'fnlwgt'` 以及一些遗失的或者是格式不正确的记录。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## 探索数据\n",
    "运行下面的代码单元以载入需要的Python库并导入人口普查数据。注意数据集的最后一列`'income'`将是我们需要预测的列（表示被调查者的年收入会大于或者是最多50,000美元），人口普查数据中的每一列都将是关于被调查者的特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 检查你的Python版本\n",
    "from sys import version_info\n",
    "if version_info.major != 2 and version_info.minor != 7:\n",
    "    raise Exception('请使用Python 2.7来完成此项目')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   workclass education_level  education-num  marital-status  \\\n",
       "0   39   State-gov       Bachelors           13.0   Never-married   \n",
       "\n",
       "      occupation    relationship    race    sex  capital-gain  capital-loss  \\\n",
       "0   Adm-clerical   Not-in-family   White   Male        2174.0           0.0   \n",
       "\n",
       "   hours-per-week  native-country income  \n",
       "0            40.0   United-States  <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为这个项目导入需要的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from IPython.display import display # 允许为DataFrame使用display()\n",
    "\n",
    "# 导入附加的可视化代码visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# 为notebook提供更加漂亮的可视化\n",
    "%matplotlib inline\n",
    "\n",
    "# 导入人口普查数据\n",
    "data = pd.read_csv(\"census.csv\")\n",
    "\n",
    "# 成功 - 显示第一条记录\n",
    "display(data.head(n=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：数据探索\n",
    "首先我们对数据集进行一个粗略的探索，我们将看看每一个类别里会有多少被调查者？并且告诉我们这些里面多大比例是年收入大于50,000美元的。在下面的代码单元中，你将需要计算以下量：\n",
    "\n",
    "- 总的记录数量，`'n_records'`\n",
    "- 年收入大于50,000美元的人数，`'n_greater_50k'`.\n",
    "- 年收入最多为50,000美元的人数 `'n_at_most_50k'`.\n",
    "- 年收入大于50,000美元的人所占的比例， `'greater_percent'`.\n",
    "\n",
    "**提示：** 您可能需要查看上面的生成的表，以了解`'income'`条目的格式是什么样的。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records: 45222\n",
      "Individuals making more than $50,000: 11208\n",
      "Individuals making at most $50,000: 34014\n",
      "Percentage of individuals making more than $50,000: 24.78%\n"
     ]
    }
   ],
   "source": [
    "# TODO：总的记录数\n",
    "n_records = data.shape[0]\n",
    "\n",
    "# TODO：被调查者的收入大于$50,000的人数\n",
    "n_greater_50k = data[data.income.str.contains('>50K')].shape[0]\n",
    "\n",
    "# TODO：被调查者的收入最多为$50,000的人数\n",
    "n_at_most_50k = data[data.income.str.contains('<=50K')].shape[0]\n",
    "\n",
    "# TODO：被调查者收入大于$50,000所占的比例\n",
    "greater_percent = np.divide(n_greater_50k,float(n_records))*100\n",
    "\n",
    "# 打印结果\n",
    "print \"Total number of records: {}\".format(n_records)\n",
    "print \"Individuals making more than $50,000: {}\".format(n_greater_50k)\n",
    "print \"Individuals making at most $50,000: {}\".format(n_at_most_50k)\n",
    "print \"Percentage of individuals making more than $50,000: {:.2f}%\".format(greater_percent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## 准备数据\n",
    "在数据能够被作为输入提供给机器学习算法之前，它经常需要被清洗，格式化，和重新组织 - 这通常被叫做**预处理**。幸运的是，对于这个数据集，没有我们必须处理的无效或丢失的条目，然而，由于某一些特征存在的特性我们必须进行一定的调整。这个预处理都可以极大地帮助我们提升几乎所有的学习算法的结果和预测能力。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 转换倾斜的连续特征\n",
    "\n",
    "一个数据集有时可能包含至少一个靠近某个数字的特征，但有时也会有一些相对来说存在极大值或者极小值的不平凡分布的的特征。算法对这种分布的数据会十分敏感，并且如果这种数据没有能够很好地规一化处理会使得算法表现不佳。在人口普查数据集的两个特征符合这个描述：'`capital-gain'`和`'capital-loss'`。\n",
    "\n",
    "运行下面的代码单元以创建一个关于这两个特征的条形图。请注意当前的值的范围和它们是如何分布的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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WUo5N8nnqleQPUVsubqbevPZE4A1l+ShPP6COgvIYljebQw0aXgX8rnTGv5/Bug+kjkDy\nlSRvonZxenkrV19rdQKQdVn+ILmHU29GnPRJxkneQW0BOIZ61Woz6ihAp5T6PAWoNz6/Isk/Uq9e\nX1lKOWMG+eu6LfC1JJ+gjr7yHmof9M90lvkC8JYkbwaOo7biPGd4RX3zVUo5rX0Xe7crzD+m7pu3\nAp8vpZw6/Jmp9NxnE3krtW/74alDhK5PDT7/wvQtCJPZnXol/IQk/8XyB8ndmzra0ZrU0cpmdR80\npwNPSvItagvLhUNB98qY9rsvpdyU5G3Uq++fo3Yl2ZTaGnMWKz64a9r1JXkydYSbr1GPKetRv88r\nWTGwXUEp5Y+tm9NhwM/a/h88SG476v/xIdTv5yDqSebRqc+H+Dm1dWBL6kMgn1Zm8LyEJJ/s5O9S\n6uASz2P5PVaDsu+f5MPUEZ0exNAJd+t2ty+15eJs6kn6HtQRlPo8yG3TduxZjdp1bHvqwBABnlJK\n+Wtb7lfUE+d9ktxEPQGf7AF7E/6ukhwHvC7JRdTA7YXMbtfHt1GP099P8hFqq9AG1JPte5ZSBk+V\n/hZ1JMBTqfvsGUx88j/Z8emb1P/5/0myFzU4+neg98hhffI6C9+tNPvKAriT29d4vqiV8mHUyuha\n6j0GJ1MPwGt1ltuDNgpTJ20j6r0SZwKbtrTVqEOv/ryt7y/t/fuprQfdbf+UzkhLLW0wQtMBE+S1\n17qp93AcAVxDHVljX2pLxy0jCU2xPw5g+YgiN1NPKn5FHaVj+wmW36O7XuoVySOpVwuvo/br/jQr\njoxzl5a/K+mMpjPRPh7K17md6c3bsq8APtTKeQ31hHqLoc/epu2Di9o2v0g9Ibtl5Jye+dq8s+xa\n1L7X51FPXs5r02tOkMcXD+Vnx5a+Y999NsX3tQv1pO+v7fdwKHCvoWV6jcLUWX596jCSJ1P/H66j\nXqXfl3oyMev7oKXtQG1VuZYVR/aZ7Lvvs85e331bdnfq/9N11C4dnwU2melviRpwf5EaPFxL/W0e\nATys5/6/B3VUpMHNvVdRb3LeE7jdUF72Bn7dlvtjW25v2shcnX3y2Gn+b5dRW1oubes6h9rS2N3e\natQTzfOo/2tHUgOW7ne1EfUixpltmT8C3wN27lHu7mhGN1BP6n9IHdlrwwmW37rNv4Y6cMI7qM/S\nGP5fnex3tTn15PvKVu6PUP8XV/gNTZLXvdtya0yz3GbUlqULqIH4RdSRjXbvLHNnanD2p/Y6iHo/\nRq/jU5v3iPbdX9P2/e70/L/pm9dV+W59+RrVazCMnCRJkiRNy3sgJEmSJPVmACFJkiSpNwMISZIk\nSb0ZQEiSJEnqzQBCkiRJUm8GEJIkSZJ6M4CQJEmS1JsBhCRJkqTeDCAkSZIk9WYAoQklOSDJ4bOw\nnr2TnDYbeZpmO5snKUm2HfW2xl2SPZJcNaJ1H5vkI53pc5O8fkTbGlk5pHEwl/XEbG1LozPK+n64\nLmj1/bNGtK05OW9Z7AwgFoF24Nx7jjf7amD3Th5WOLFbgH4PbAKc0vcDSXZMcu40y5zbDlTd159X\nMa/D25j3fdv2xaB8Nye5IskvkuybZIuhxb8I3LPnemca2D0DeONM8t4zHxNVNr3LIS101hOzp11c\nOHaaZYbrhZKkd/3TMx8ju4Aygzzs0SnfTUn+nOTEJPsk2Who8f8AHtVzvYM65849s/JQ4KMzyXuP\nPExWP/UuxzhbY74zoIWplPKX+c7DTJRSbgIuHtHq3wF8rDN984i2s8qSrFlKuWEVVnE/4I/A+sCD\ngNcApyZ5UinlewCllL8Cf13lzHYkWauUcn0p5Y+zud6pjKIc0jhZbPXECLwE6LaKrMqxd2SSrAak\n1ZMr4xpgSyDA7agn828AXpLkUaWUXwGUUq4CZrVVt1M3XDab653KKMqxFNkCsQglWSvJu5Ocl+S6\nJL9N8q9t3upJPp3knCR/TXJWkn9vB5DB5w9IcniStyS5JMlVSf43yTrDywzeU6PxV3auRGzeZ1s9\ny7Neks+0fFyU5P+1/B3QWWb3JCckuTLJpUm+lGTTzvwVriR0rm48JslPk1zTrpo8ZCV2+ZWllIs7\nr0s72719kk+2PF2Z5HvdqxlJ7pTk80nOb/vol0le0Jk/2b691dWZKcr4xCTHJ7ke2LnNe0qSk5Jc\n276ffZKs1aOsl7Yynl1K+TKwI3AysH+S1du6V+j6k+RuSQ5N8se2n3+dZLc2+5z294SW12MH5W7f\n8RuSnA+c39InuoK5fpLPtd/HxRm6IpcJWhfSuXKX5a1MX2rLnjtROVray5KcneT69vclE2zrpe33\nd3X739sdaYHJEqsnJijf2kn+s+Xt2iTHJXlEZ/6aSfZLcmEr/++TvLcz/xmprax/bceu7yXZeIbZ\n+PNQ3XB5Z/2bJvlCkj+11zeSbNWZv2U7bl7cjiU/S/LkzvxjgXsAHxjsz5Y+0XFrhfpisEyrG04D\nrgfu0+a9IMnpbZ+dmeTfenwXpZXvolLKGaWUzwEPB/4MfLyTjxW6/iR5QJKjU1u0r0ry8yQ7Jdkc\nOKYtdlnL+wGDcif5WJL/SHIZ8KOWPlFrzF3afr2m/c67rWETti5kxfpisvppuByrJXlr+w1dl+TU\nJLtOsK1nJjmq5ef0JI+bZr8uagYQi9OBwPOB11IPCsuAP7V5qwEXAM9u894MvAl4wdA6HkW9wvwY\n4JnA44H3TbK9VwM/Af6X2k1oE2qXob7bms4HW36eDjwW2Ab4+6Fl1gL2anl+MnBn4PM91v0eYE/g\nIcDlwEFJMsP8Tait5xvApi1PDwa+D3w3ySZtsdsAP2vz7wfsC3wiyWPa/Mn27Uy8D3gLcG/gp0l2\nBg4CPtK2+ULgWcC7Z1rGdsXqw9SuPg+eZLGPAusCO7XtvYZasQBs1/7uQi3bMzqfexTwwDbvMUzu\ntcCvqN/hXsC7kzxjiuWHPbT9fUnLw0MnWijJ06n77D+B+1O/q48mecrQom8DDqX+Fr9IDa7uPoP8\nSHNhqdUTw94P/CP1+PZg4FTgW51j779S65TdgK3asmcAJLkL8AXqProP8Ejgs6uYn1skWZd6gnwt\ndR8+HLgI+E6bB7WV95vA46j7+MvAV5Lcu81/BvXCyjtYvj9n4jbAW4GXAfcFzku9IPJu6jHsPsDr\nqC0Jr5hpGdtV+o8Dj0yy4SSL/R+13NsBWwN7U/fJ76m/J6h1xibU38/A7tTWjr+n/oYn83bgsLbu\nTwKfGQ4YpjFV/dT1auD/UffVA4CvUr+rrYeW2wfYj/p9ngB8Icn6M8jP4lJK8bWIXtQDYQF2mcFn\n3gt8pzN9APUEb/1O2u7AdcB6nWUO78w/FvjISmxrb+C0KZZfn3p1ZLdO2nrUiu6AKT5377YfNmvT\nm7fpbdv0jm16585nduh+pue+O7ftl6s6rze1eY9u0+sMfeYU4N+nWOcXgE9NtW87+b9zJ22yMj5z\n6LPfB946lPa0ltdMkqdbbW+Cff3sNr0HcFVn/i+AvSZZ7wp5HvoNXgasPZS+wr5o+/+ooWU+Bfyw\nM12AZ03wvb1+mmWGy/EjYP8J8jm8rfd0ptegNu/v3vc35cvXqF8ssXpieFvUOuJ64Pmd+asDvwHe\n1ab3A46e6JhHvRhRgHuswj4u1C6Q3brhuW3eC4Gzuttu+bt8cBydZJ3HAW/pTK9wHGtpKxy3WtqO\ndI7fbZkCbDO03O+A5w2lvQY4fYo83Wp7nXm7tO1sN9H3CFwBLJvksyvkeeg39IsJll9hX7TP/s/Q\nMt8BPtfeb87Edc8tdcEUywyX4wLgbRPkc3hbL+vM37SlPWJlf2ML/eU9EIvPg6l98I+ZbIEkLwde\nTG3+XAdYEzhvaLFflHoFYeAn1Kv8W1JPCHvpua3Bsn9PveIy8DLgtPaZ4weJpZSrMzQCQmrXo72o\nVxruSL06AXB3WveXSXTLcmH7u9E0nxn2IeDTnelBP/1tqFfeLxtq1LgNdT+S2u1nT+rVr02Btan7\n+dgZbH86Jw5NbwNsl+QNnbTVqN/PXahXhGZiULgyyfx9gY8n2YVaYX+1lHJSj/WeVkq5rsdyP5lg\neiYtEH3dB9h/KO2HwFOH0m75TZVSbmzN7MM3E0rzaUnVE6WUg4YW27Kt40eDhFLKTUl+Qr3aDjXg\nOAo4M8m3gSOAb5ZSbgZ+Tj3ZPK3N+w5wSJl5P/v/B3yrM31J+7sNsAVw5VDdsC7L64b1qHXak6lX\nv9ek1h299+s0bqQzqEhrJbgbtQW8e0/fGiw/xs/UdHXDh4BPJVlGrRu+XEr5dY/19qk/YOK64Uk9\nP9tLktsBd6XzW2t+CDxxKG2y840lyQBiiUnyj9QuGK8Hfky9AvBKalPufG/rRGoAMHAJPUbBaQfa\nI6kH+ecBl1K7MP2AWplNpXtT2+AgN9Oue5eXUs6eIH01ahmGu1tB3RdQ983rqE2gp1KvUr2b6Q8q\ngxu1uwf2NSdZ9uoJ8vV24EsTLLsyN6INKuTfTjSzlPLpJEdSD6aPBX6c5D2llL2nWe9wvldW4dYV\n4GT7amXX3zV8o2TB7qBaRBZhPTET9TJzKT9rfe13pnbBOhD4eZLHtWDj8cD21G5ZLwLek3pD8M9n\nsK2Lp6gbTqF2nxo2uAD1H9Qr+K+ntlZcA3yG6eu0m+l3vLuurHjT9OAY9XLq9zAb7kvd3+dONLOU\nsneSg4AnUL+HvZK8vJQyfKFm2GzUDbeqQ5PMZr0AU9QNpZTSgsclWzcYQCw+p1B/kDux4pWPgUcA\nPy2ldMfS33KC5R6QZL1SyuAfdXtqk/BvJtnu9dQm2JXZFnDLqDcrHGyT/Ib6T/dQ2glq6yN6/05e\n7k0NGN5USjmnLTOKK9Az9TNgY+DmUsqEJ9fUffT1Uspn4Zb7Jv6W5fcIwMT7dnCiv0nn/XB/y6ny\nde9JKrYZaS0or6F+F5MOUVhKOZ/aB/WTreXj1dRm4OvbIsPlm4ntJ5j+VWf6Mjr9g1NvhBzuL3xD\njzz8itrNrdva9Ajg9JlkVloAllQ9MYHftG3tMMhLO1Y9nNrvfrCuK4FDgEPaTbrHAX8DnFlqP5Of\nAD9J8g7gl9SW4pkEEJP5GfAc4A+llMmG/X4E8JlSB6sgyaDl+szOMpPVDesmuV0pZXChatq6oZRy\nSZILgS1LKZ/pX5SJtb79Lwe+N1XLTSnlLGqAtF9r+XgxtaV3tuqG/YemB3VDtw4dGN5P0+ahlHJF\n2287UFtRBsa+bjCAWGRKKWcmOZjaLPhq6oFqM2DzdpJ6JrBHkidQD8K7UW/i+tPQqtag3vz5Dmrz\n3Hup/Qkni/zPpXaL2Zx6Ff2PM9jWVOW5Ksn+wPuS/IHaveYt1MpvEN3/jtrv9lVJ/pva1eSdfbcx\nQt+hNmsemuTfgV9TuwjtQu3f+wPqPvrH1NFB/gD8C7Vp++TOes7l1vv2bOqNZnsn2ZPax/ItPfP1\nDuDwJOcBB1Obsu9P7af679N8dqMka1DvTXkg8G/U7hBPLJMMAZhkX2qXgzOpQ/ztwvID66XUfsI7\np45+dG2Z+dCP2yd5I/VEYEfqTXXP7cz/LnXklx8DN1FbeK4dWse5wGOSfI96ZW6i3+gHqCM1nQR8\nu5XjuYymu5Q0MkutnpigfFe3k9FBvXEO9Vi1Me1ZAUleS61PTqFeQPgnauvH+Um2p7aWHklt4Xgw\ntXvPbJ0QHkRtWTg0yduoddjdgF2Bj7eT6jOBpyc5tOVvL2oXpq5zgb9P8jnqcesPwE+pV+jfk+TD\n1Bt2+94EvRfwX6nPMjqC2nLxEGDTUsp7pvhc2o3nALdn+TCut+fWXTwHH1iH2srypVaOjWnBZFvk\nPGod/6QkXwf+OtRdro9nJDmB2iX4WdSWpodBDUSTHAe8oV2ovD11UJWuvvXTB4B3JDmL2r1qd2rP\ng5UZ1XHJWLJNK0vc86lXWfajnrQeQP3nAPgE9aTx/6ijAGxOHeVo2PeoV1yOoY4o8F1gqpPL/6BG\n66dTI/u7z2Bb03k9tTvSYS0/p1Kbsa8FaFc3llFvBD6dehB87UpsZ1a1K1hPpO67/6GO8HEwcC+W\n9398F/X+jm9Sb26+mlq5dN1q35b6LIfdqF28fk7tkvSmnvk6ktoPdKe27eOp92H8rsfHf0mtdE+m\nBiInAw8Y0nTYAAAfqUlEQVQspXx/is+sBvxXy/9R1Ap5WcvLjdTRUF5M3SeH9inDkA9Rg5mTqfvz\nbaWUQzrzX0dtvTqWGmR8iloxMLTMTtSg7GQmUEr5GjXA+7dWllcDryilfH0l8izNt6VWTwx7A3UU\ntP+lBgkPpN40PrjH60rqPQrHUwOorYEnlFKuAf5CvaJ8OPXq+AeBd5Y6POkqa9t4JPW49CXq/j8Q\n2IDlgdNrqcepH1Drh+Pa+663UQOP39CuqJf6rJznUkdvOhV4KXW0pT75+hT1Bu/nUeuVH7TPnzPN\nR9el1gsXUvfna4GvA/cv7RkQE7iJWt4DqHXjV6ktPq9tebmAWpfvQ60zVuYBhHtTR3P6BfDPwAtK\nKSd05r+w/T2B+jtc4SLcDOqn/ahBxPup920+nTp4yWy0Vi1aqedAGietKffOpZQnT7fsfEiyNvXq\nxAdKKbNR0UiSZmCh1xOS5pddmDTvkjyY2i3peOC21CtLt6VeXZIkSdICMm9dmJIclOSMJKcl2X9w\nd3yq/VKfAvuLdJ4cnGSX9pmzW7/wQfodU5/+d1b7u8F8lEmr5LXUriXfpfaVfGS7MVfSmLF+kKSF\nbWQBRI+D9EHU0XUeQB0X+sUt/QnUh+BsRe2b97G2vtWB/27z7ws8J8lgeMk9gaNLKVtR75K/pfLQ\nrZVS9lhIzdKllJNLKduWUm5bStmglLJTz+cISFqErB8WvoVWT0haWEbZAnFiu4r06DZ05QpKKUeU\nhtp1ZbM2a1fq0GallHIccIfUR9NvB5xdSvltKeV66tN8d+185sD2/kDqzbaSpIXJ+kGSFrFR3gPx\nt9SrQa8C/jvJZ4EDSikXdhdqTdPPo454AvVpvb/vLHJ+S5so/WHt/cadkRcupnaBuZUkL6VetWK9\n9dbb5t73vveMC3XS5ZfPaPlt7nSnGW9DkkbppJNO+kMpZcN5zMKSrB9gZnWE9YOkhaZv/TCyAKKN\nGX84dTz6Danj7/4uyd+VUo7vLPpR4PttzPzZ2G5JMuHQUqWUT1IfdsW2225bTjzxxBmvPwceOP1C\nHScuWzbjbUjSKLVnhMybpVo/wMzqCOsHSQtN3/phpKMwJbk9dSz7PahjQ7+QOl7vYP5ewIbAyzof\nu4A67vHAZi1tzUnSAS5Jskkp5aLWnD08BrwkaQGxfpCkxWuUN1F/jvrwli2A55dSHlVK+Uwp5do2\n/8XAzsBzSik3dz56GPD8NtrG9sBfWvPzCcBWSbZIsha14jms85nBpZxlrNzDqiRJc8D6QZIWt1G2\nQBwM7NGe9DeRj1MfFvaTdg/dV0op76A+Xv2J1EfeXwO8AOoTA5O8ivro+dWB/Uspv2zrei9wcJIX\ntXU+ezRFkiTNAusHSVrERnkPxGHTzJ9w223UjVdOMu8IagUynH458JiVyKYkaY5ZP0jS4jZvD5KT\nJEmStPgYQEiSJEnqzQBCkiRJUm8GEJIkSZJ6M4CQJEmS1JsBhCRJkqTeDCAkSZIk9WYAIUmSJKk3\nAwhJkiRJvRlASJIkSerNAEKSJElSbwYQkiRJknozgJAkSZLUmwGEJEmSpN4MICRJkiT1ZgAhSZIk\nqTcDCEmSJEm9GUBIkiRJ6s0AQpIkSVJvBhCSJEmSejOAkCRJktSbAYQkSZKk3gwgJEmSJPVmACFJ\nkiSpNwMISZIkSb0ZQEiSJEnqzQBCkiRJUm8GEJIkSZJ6M4CQJEmS1JsBhCRJkqTeDCAkSZIk9WYA\nIUmSJKk3AwhJkiRJvRlASJIkSerNAEKSJElSbwYQkiRJknozgJAkSZLUmwGEJEmSpN4MICRJkiT1\nZgAhSZIkqTcDCEmSJEm9GUBIkiRJ6s0AQpIkSVJvBhCSJEmSejOAkCRJktSbAYQkSZKk3gwgJEmS\nJPVmACFJkiSpNwMISZIkSb0ZQEiSJEnqzQBCkiRJUm8GEJIkSZJ6M4CQJEmS1JsBhCRJkqTeDCAk\nSZIk9WYAIUmSJKk3AwhJkiRJvRlASJIkSerNAEKSJElSbwYQkiRJknozgJAkSZLUmwGEJEmSpN4M\nICRJkiT1Nq8BRJL9k1ya5LRO2t5JLkhySns9sTPvjUnOTnJGkp076dskObXN2y9J5roskqTZY/0g\nSQvXfLdAHADsMkH6h0spW7fXEQBJ7gvsBtyvfeajSVZvy38MeAmwVXtNtE5J0uJxANYPkrQgzWsA\nUUr5PvDHnovvCnyhlHJdKeUc4GxguySbALcrpRxXSinAZ4CnjSbHkqS5YP0gSQvXGvOdgUn8S5Ln\nAycCryul/AnYFDius8z5Le2G9n44fUHIgQfOaPmybNmIciJJS8KSqR8kabGa7y5ME/kYcE9ga+Ai\n4IOzteIkL01yYpITL7vsstlarSRpblg/SNICsOACiFLKJaWUm0opNwP/A2zXZl0A3K2z6GYt7YL2\nfjh9onV/spSybSll2w033HD2My9JGhnrB0laGBZcANH6rA48HRiMwHEYsFuStZNsQb0Z7vhSykXA\nFUm2b6NrPB84dE4zLUkaOesHSVoY5vUeiCSfB3YE7pzkfGAvYMckWwMFOBd4GUAp5ZdJDgZOB24E\nXllKuamt6hXUETvWAb7ZXpKkRcr6QZIWrnkNIEopz5kg+dNTLL8PsM8E6ScC95/FrEmS5pH1gyQt\nXAuuC5MkSZKkhcsAQpIkSVJvBhCSJEmSejOAkCRJktSbAYQkSZKk3gwgJEmSJPVmACFJkiSpNwMI\nSZIkSb0ZQEiSJEnqzQBCkiRJUm8GEJIkSZJ6M4CQJEmS1JsBhCRJkqTeDCAkSZIk9WYAIUmSJKk3\nAwhJkiRJvRlASJIkSerNAEKSJElSbwYQkiRJknozgJAkSZLUmwGEJEmSpN6mDSCS7JBkvfZ+9yQf\nSnKP0WdNkrSQWT9I0njq0wLxMeCaJA8CXgf8BvjMSHMlSVoMrB8kaQz1CSBuLKUUYFfgI6WU/wZu\nO9psSZIWAesHSRpDa/RY5sokbwR2Bx6ZZDVgzdFmS5K0CFg/SNIY6tMC8Y/AdcCLSikXA5sBHxhp\nriRJi4H1gySNoWlbIFql8KHO9O+wj6skjT3rB0kaT5MGEEmuBMpk80sptxtJjiRJC5r1gySNt0kD\niFLKbQGSvBO4CPgsEOC5wCZzkjtJ0oJj/SBJ463PPRBPLaV8tJRyZSnlilLKx6gjbkiSxpv1gySN\noT4BxNVJnptk9SSrJXkucPWoMyZJWvCsHyRpDPUJIP4JeDZwSXv9Q0uTJI036wdJGkNTjsKUZHXg\n6aUUm6QlSbewfpCk8TVlC0Qp5SbgOXOUF0nSImH9IEnjq8+TqH+U5CPAF+n0bS2l/GxkuZIkLQbW\nD5I0hvoEEFu3v+/opBXg0bOfHUnSImL9IEljqM+TqHeai4xIkhYX6wdJGk/TjsKU5PZJPpTkxPb6\nYJLbz0XmJEkLl/WDJI2nPsO47g9cSR2q79nAFcD/jjJTkqRFwfpBksZQn3sgtiylPLMz/fYkp4wq\nQ5KkRcP6QZLGUJ8WiL8mecRgIskOwF9HlyVJ0iJh/SBJY6hPC8Q/Awd2+rX+CdhjZDmSJC0W1g+S\nNIb6jMJ0CvCgJLdr01eMPFeSpAXP+kGSxlOfUZjeneQOpZQrSilXJNkgybvmInOSpIXL+kGSxlOf\neyCeUEr582CilPIn4Imjy5IkaZGwfpCkMdQngFg9ydqDiSTrAGtPsbwkaTxYP0jSGOpzE/VBwNFJ\nBmN7vwA4cHRZkiQtEtYPkjSG+txE/b4kPwce25LeWUo5crTZkiQtdNYPkjSe+rRAAPwKuLGU8p0k\n6ya5bSnlylFmTJK0KFg/SNKY6TMK00uAQ4BPtKRNga+NMlOSpIXP+kGSxlOfm6hfCewAXAFQSjkL\n2GiUmZIkLQrWD5I0hvoEENeVUq4fTCRZAyijy5IkaZGwfpCkMdQngPhekjcB6yR5HPAl4OujzZYk\naRGwfpCkMdQngNgTuAw4FXgZcATwllFmSpK0KFg/SNIY6jOM683A/7QXAEl2AH40wnxJkhY46wdJ\nGk+TBhBJVgeeTR1V41ullNOSPBl4E7AO8OC5yaIkaSGxfpCk8TZVC8SngbsBxwP7JbkQ2BbYs5Ti\nMH2SNL6sHyRpjE0VQGwLPLCUcnOS2wAXA1uWUi6fm6xJkhYo6wdJGmNT3UR9fevfSinlWuC3Vg6S\nJKwfJGmsTdUCce8kv2jvA2zZpgOUUsoDR547SdJCZP0gSWNsqgDiPnOWC0nSYmL9IEljbNIAopRy\n3lxmRJK0OFg/SNJ46/MgOUmSJEkCDCAkSZIkzcCkAUSSo9vf941q40n2T3JpktM6aXdMclSSs9rf\nDTrz3pjk7CRnJNm5k75NklPbvP2SZFR5lqRxZ/0gSeNtqhaITZL8HfDUJA9O8pDua5a2fwCwy1Da\nnsDRpZStgKPbNEnuC+wG3K995qPtaagAHwNeAmzVXsPrlCTNHusHSRpjU43C9DbgrcBmwIeG5hXg\n0au68VLK95NsPpS8K7Bje38gcCzwhpb+hVLKdcA5Sc4GtktyLnC7UspxAEk+AzwN+Oaq5k+SNCHr\nB0kaY1ONwnQIcEiSt5ZS3jmHedq4lHJRe38xsHF7vylwXGe581vaDe39cLokaQSsHyRpvE3VAgFA\nKeWdSZ4KPLIlHVtKOXy02bpl2yVJma31JXkp8FKAu9/97rO1WkkaS9YPkjSeph2FKcl7gFcDp7fX\nq5O8e4R5uiTJJm3bmwCXtvQLgLt1ltuspV3Q3g+n30op5ZOllG1LKdtuuOGGs55xSRon1g+SNJ76\nDOP6JOBxpZT9Syn7U29Ae/II83QYsKy9XwYc2knfLcnaSbag3gx3fGvOviLJ9m10jed3PiNJGh3r\nB0kaQ9N2YWruAPyxvb/9bG08yeepN8TdOcn5wF7Ae4GDk7wIOA94NkAp5ZdJDqZe5boReGUp5aa2\nqldQR+xYh3pznDfISdLcsH6QpDHTJ4B4D3BykmOAUPu67jkbGy+lPGeSWY+ZZPl9gH0mSD8RuP9s\n5EmS1Jv1gySNoT43UX8+ybHAQ1vSG0opF480V5KkBc/6QZLGU68uTK0f6WEjzoskaZGxfpCk8dPn\nJmpJkiRJAgwgJEmSJM3AlAFEktWT/HquMiNJWhysHyRpfE0ZQLRh8M5I4mM5JUm3sH6QpPHV5ybq\nDYBfJjkeuHqQWEp56shyJUlaDKwfJGkM9Qkg3jryXEiSFiPrB0kaQ32eA/G9JPcAtiqlfCfJusDq\no8+aJGkhs36QpPE07ShMSV4CHAJ8oiVtCnxtlJmSJC181g+SNJ76DOP6SmAH4AqAUspZwEajzJQk\naVGwfpCkMdQngLiulHL9YCLJGkAZXZYkSYuE9YMkjaE+AcT3krwJWCfJ44AvAV8fbbYkSYuA9YMk\njaE+AcSewGXAqcDLgCOAt4wyU5KkRcH6QZLGUJ9RmG5OciDwU2rT9BmlFJuoJWnMWT9I0niaNoBI\n8iTg48BvgABbJHlZKeWbo86cJGnhsn6QpPHU50FyHwR2KqWcDZBkS+AbgBWEJI036wdJGkN97oG4\nclA5NL8FrhxRfiRJi4f1gySNoUlbIJI8o709MckRwMHUPq7/AJwwB3mTJC1A1g+SNN6m6sL0lM77\nS4BHtfeXAeuMLEeSpIXO+kGSxtikAUQp5QVzmRFJ0uJg/SBJ463PKExbAP8CbN5dvpTy1NFlS5K0\n0Fk/SNJ46jMK09eAT1OfLnrzaLMjSVpErB8kaQz1CSCuLaXsN/KcSJIWG+sHSRpDfQKIfZPsBXwb\nuG6QWEr52chyJUlaDKwfJGkM9QkgHgA8D3g0y5uoS5uWJI0v6wdJGkN9Aoh/AO5ZSrl+1JmRJC0q\n1g+SNIb6PIn6NOAOo86IJGnRsX6QpDHUpwXiDsCvk5zAin1cHaZPksab9YMkjaE+AcReI8+FJGkx\nsn6QpDE0bQBRSvneXGREkrS4WD9I0njq8yTqK6mjagCsBawJXF1Kud0oMyZJWtisHyRpPPVpgbjt\n4H2SALsC248yU5Kkhc/6QZLGU59RmG5Rqq8BO48oP5KkRcj6QZLGR58uTM/oTK4GbAtcO7IcSZIW\nBesHSRpPfUZhekrn/Y3AudRmaknSeLN+kKQx1OceiBfMRUYkSYuL9YMkjadJA4gkb5vic6WU8s4R\n5EeStMBZP0jSeJuqBeLqCdLWA14E3AmwgpCk8WT9IEljbNIAopTywcH7JLcFXg28APgC8MHJPidJ\nWtqsHyRpvE15D0SSOwKvBZ4LHAg8pJTyp7nImCRp4bJ+kKTxNdU9EB8AngF8EnhAKeWqOcuVJGnB\nsn6QpPE2VQvE64DrgLcAb64PGQUg1JvkbjfivEmSFibrB42FHHjgjJYvy5aNKCfSwjLVPRAzekq1\nJGk8WD9I0nizEpAkSZLUmwGEJEmSpN4MICRJkiT1NuUwrlrYvLlLkiRJc80WCEmSJEm9GUBIkiRJ\n6s0AQpIkSVJvBhCSJEmSejOAkCRJktSbAYQkSZKk3gwgJEmSJPVmACFJkiSpNwMISZIkSb0ZQEiS\nJEnqzQBCkiRJUm8GEJIkSZJ6M4CQJEmS1JsBhCRJkqTeDCAkSZIk9WYAIUmSJKm3BRtAJDk3yalJ\nTklyYku7Y5KjkpzV/m7QWf6NSc5OckaSnecv55KkUbJ+kKT5tWADiGanUsrWpZRt2/SewNGllK2A\no9s0Se4L7AbcD9gF+GiS1ecjw5KkOWH9IEnzZKEHEMN2BQ5s7w8EntZJ/0Ip5bpSyjnA2cB285A/\nSdL8sH6QpDmykAOIAnwnyUlJXtrSNi6lXNTeXwxs3N5vCvy+89nzW9oKkrw0yYlJTrzssstGlW9J\n0mhZP0jSPFpjvjMwhUeUUi5IshFwVJJfd2eWUkqSMpMVllI+CXwSYNttt53RZyVJC4b1gyTNowXb\nAlFKuaD9vRT4KrXJ+ZIkmwC0v5e2xS8A7tb5+GYtTZK0xFg/SNL8WpABRJL1ktx28B54PHAacBiw\nrC22DDi0vT8M2C3J2km2ALYCjp/bXEuSRs36QZLm30LtwrQx8NUkUPP4f6WUbyU5ATg4yYuA84Bn\nA5RSfpnkYOB04EbglaWUm+Yn65KkEbJ+kKR5tiADiFLKb4EHTZB+OfCYST6zD7DPiLMmSZpH1g+S\nNP8WZBcmSZIkSQuTAYQkSZKk3hZkFyZJkqTZlgMPnH4hSdOyBUKSJElSb7ZASJIkzYKZtHCUZcum\nX0haoGyBkCRJktSbAYQkSZKk3gwgJEmSJPVmACFJkiSpNwMISZIkSb0ZQEiSJEnqzQBCkiRJUm8G\nEJIkSZJ6M4CQJEmS1JsBhCRJkqTeDCAkSZIk9WYAIUmSJKk3AwhJkiRJvRlASJIkSerNAEKSJElS\nbwYQkiRJknozgJAkSZLUmwGEJEmSpN4MICRJkiT1ZgAhSZIkqTcDCEmSJEm9GUBIkiRJ6s0AQpIk\nSVJvBhCSJEmSejOAkCRJktSbAYQkSZKk3gwgJEmSJPVmACFJkiSpNwMISZIkSb2tMd8ZkCRVOfDA\n3suWZctGmBNJkiZnC4QkSZKk3gwgJEmSJPVmACFJkiSpN++BWGBm0gdakiRJmmu2QEiSJEnqzQBC\nkiRJUm8GEJIkSZJ68x4ISZK0aHnvoDT3bIGQJEmS1JsBhCRJkqTeDCAkSZIk9WYAIUmSJKk3AwhJ\nkiRJvTkKkyRJ0gI309GmyrJlI8qJZAuEJEmSpBmwBUKSJGmO+fwKLWa2QEiSJEnqzQBCkiRJUm8G\nEJIkSZJ6M4CQJEmS1JsBhCRJkqTeHIVJs8LxqSVJksaDLRCSJEmSejOAkCRJktSbXZgkSZKWmJl0\nLbZbsWbKAEKT8imZkiRJGmYXJkmSJEm9GUBIkiRJ6m3JdGFKsguwL7A68KlSynvnOUuSpAXA+mFx\nsfustPAtiQAiyerAfwOPA84HTkhyWCnl9PnNmSRpPo1r/eCzeSSN0pIIIIDtgLNLKb8FSPIFYFdg\nSVcQkqRpLdj6YVxO8m1RWHoc4UlLJYDYFPh9Z/p84GHzlBeNwCgPVuNSiUtjyvphBAwK1NdirmMX\nc95HLaWU+c7DKkvyLGCXUsqL2/TzgIeVUl41tNxLgZe2yXsBZ6zE5u4M/GEVsruYjEtZLefSYjmn\nd49SyoazmZmFao7rBxif39903A/LuS+Wc18st1D3Ra/6Yam0QFwA3K0zvVlLW0Ep5ZPAJ1dlQ0lO\nLKVsuyrrWCzGpayWc2mxnBoyZ/UD+L0MuB+Wc18s575YbrHvi6UyjOsJwFZJtkiyFrAbcNg850mS\nNP+sHyRpli2JFohSyo1JXgUcSR2mb/9Syi/nOVuSpHlm/SBJs29JBBAApZQjgCPmYFOr3MS9iIxL\nWS3n0mI5tYI5rB/A72XA/bCc+2I598Vyi3pfLImbqCVJkiTNjaVyD4QkSZKkOWAAMQNJdklyRpKz\nk+w53/npI8ndkhyT5PQkv0zy6pZ+xyRHJTmr/d2g85k3tjKekWTnTvo2SU5t8/ZLkpa+dpIvtvSf\nJtl8rsvZ8rF6kpOTHN6ml1wZW17ukOSQJL9O8qskD1+KZU3yb+03e1qSzye5zVIoZ5L9k1ya5LRO\n2pyUK8myto2zkozPgOVzIIuwfpipUf92F4vMQb26WLTj8vFJft72xdtb+tjtCxjteciCU0rx1eNF\nvfnuN8A9gbWAnwP3ne989cj3JsBD2vvbAmcC9wXeD+zZ0vcE3tfe37eVbW1gi1bm1du844HtgQDf\nBJ7Q0l8BfLy93w344jyV9bXA/wGHt+klV8a2/QOBF7f3awF3WGplpT786xxgnTZ9MLDHUign8Ejg\nIcBpnbSRlwu4I/Db9neD9n6D+fodL6UXi7R+WIlyjvS3u1hezEG9ulheLd/rt/drAj9t5Rm7fdHK\nMLLzkIX2mvcMLJYX8HDgyM70G4E3zne+VqIchwKPoz4kaZOWtglwxkTloo5c8vC2zK876c8BPtFd\npr1fg/pglMxxuTYDjgYe3fnHXVJlbNu+PfXEOkPpS6qsLH968B1bHg4HHr9UyglszoonYSMvV3eZ\nNu8TwHPm+je8FF8skfqhZ1lH9ttdrC9GUK8uxhewLvAz6pPex25fMOLzkIX2sgtTf4MTmoHzW9qi\n0boyPJh6hWDjUspFbdbFwMbt/WTl3LS9H05f4TOllBuBvwB3mvUCTO0/gX8Hbu6kLbUyQr1ScRnw\nv62Z9FNJ1mOJlbWUcgHwH8DvgIuAv5RSvs0SK2fHXJRr0R/DFrBx3rez+dtddEZYry4ardvOKcCl\nwFGllHHdF6M+D1lQDCDGRJL1gS8DrymlXNGdV2qYW+YlY7MgyZOBS0spJ022zGIvY8ca1C4EHyul\nPBi4mtoseoulUNbWT3RXasB0V2C9JLt3l1kK5ZzIUi2Xlr5x++0u5Xp1JkopN5VStqZegd8uyf2H\n5i/5fTFm5yGAAcRMXADcrTO9WUtb8JKsST3IHVRK+UpLviTJJm3+JtQrBzB5OS9o74fTV/hMkjWo\n3Wwun/2STGoH4KlJzgW+ADw6yedYWmUcOB84v13hATiEGlAstbI+FjinlHJZKeUG4CvA37H0yjkw\nF+VatMewRWCc9+1s/nYXjTmoVxedUsqfgWOAXRi/fTEX5yELigFEfycAWyXZIsla1JsTD5vnPE2r\n3b3/aeBXpZQPdWYdBixr75dR+3AO0ndLHcllC2Ar4PjWBHdFku3bOp8/9JnBup4FfLdF2nOilPLG\nUspmpZTNqd/Ld0spu7OEyjhQSrkY+H2Se7WkxwCns/TK+jtg+yTrtvw9BvgVS6+cA3NRriOBxyfZ\noLXwPL6ladUtyvphlszmb3dRmKN6dVFIsmGSO7T361DvBfk1Y7Yv5ug8ZGGZ75swFtMLeCJ1tIXf\nAG+e7/z0zPMjqE1mvwBOaa8nUvtEHw2cBXwHuGPnM29uZTyDzt3/wLbAaW3eR1j+IMLbAF8CzqaO\nHnDPeSzvjiy/eWmplnFr4MT2nX6NOqLOkisr8HZqRXQa8FnqaBWLvpzA56n3ddxAbVF60VyVC3hh\nSz8beMF8/YaX4otFWD+sRBlH+ttdLC/moF5dLC/ggcDJbV+cBrytpY/dvuiUY0dGcB6y0F4+iVqS\nJElSb3ZhkiRJktSbAYQkSZKk3gwgJEmSJPVmACFJkiSpNwMISZIkSb0ZQEirIMkxSXYeSntNko9N\n8ZmrRp8zSdJ8sn7QUmYAIa2az1MfGtO1W0uXJI0v6wctWQYQ0qo5BHhSe/osSTYH7gqcnOToJD9L\ncmqSXYc/mGTHJId3pj+SZI/2fpsk30tyUpIjk2wyF4WRJM0a6wctWQYQ0ioopfyR+rTfJ7Sk3YCD\ngb8CTy+lPATYCfhgeyz9tJKsCfwX8KxSyjbA/sA+s513SdLoWD9oKVtjvjMgLQGDZupD298XAQHe\nneSRwM3ApsDGwMU91ncv4P7AUa1OWR24aPazLUkaMesHLUkGENKqOxT4cJKHAOuWUk5qTc0bAtuU\nUm5Ici5wm6HP3ciKrYCD+QF+WUp5+GizLUkaMesHLUl2YZJWUSnlKuAYalPy4Oa42wOXtsphJ+Ae\nE3z0POC+SdZOcgfgMS39DGDDJA+H2mSd5H4jLYQkadZZP2ipsgVC/7+dOzZBAIaiKPr+BC7nYOIO\nIthYuIZgoYKdhVvYxEJBsPqFIso5ZSCQFCFcCOE9lknWef64sUiyqap9km2S0+uEMcalqlZJDknO\nSXaP8WtVTZPMq2qS+zmdJTl+fBcAvJv7gb9TY4xvrwEAAPgRnjABAABtAgIAAGgTEAAAQJuAAAAA\n2gQEAADQJiAAAIA2AQEAALQJCAAAoO0Ga7UEP7pgqRcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcfbf3ca550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将数据切分成特征和对应的标签\n",
    "income_raw = data['income']\n",
    "features_raw = data.drop('income', axis = 1)\n",
    "\n",
    "# 可视化原来数据的倾斜的连续特征\n",
    "vs.distribution(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于高度倾斜分布的特征如`'capital-gain'`和`'capital-loss'`，常见的做法是对数据施加一个<a href=\"https://en.wikipedia.org/wiki/Data_transformation_(statistics)\">对数转换</a>，将数据转换成对数，这样非常大和非常小的值不会对学习算法产生负面的影响。并且使用对数变换显著降低了由于异常值所造成的数据范围异常。但是在应用这个变换时必须小心：因为0的对数是没有定义的，所以我们必须先将数据处理成一个比0稍微大一点的数以成功完成对数转换。\n",
    "\n",
    "运行下面的代码单元来执行数据的转换和可视化结果。再次，注意值的范围和它们是如何分布的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CibbVdOff1H3BZGKXUM0CszA/9KOcZs48o/a134XSBWsx8JuIeEYtNv4J2I7S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\nlt95AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcfbf3ca7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对于倾斜的数据使用Log转换\n",
    "skewed = ['capital-gain', 'capital-loss']\n",
    "features_raw[skewed] = data[skewed].apply(lambda x: np.log(x + 1))\n",
    "\n",
    "# 可视化经过log之后的数据分布\n",
    "vs.distribution(features_raw, transformed = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 规一化数字特征\n",
    "除了对于高度倾斜的特征施加转换，对数值特征施加一些形式的缩放通常会是一个好的习惯。在数据上面施加一个缩放并不会改变数据分布的形式（比如上面说的'capital-gain' or 'capital-loss'）；但是，规一化保证了每一个特征在使用监督学习器的时候能够被平等的对待。注意一旦使用了缩放，观察数据的原始形式不再具有它本来的意义了，就像下面的例子展示的。\n",
    "\n",
    "运行下面的代码单元来规一化每一个数字特征。我们将使用[`sklearn.preprocessing.MinMaxScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)来完成这个任务。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.30137</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.02174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age   workclass education_level  education-num  marital-status  \\\n",
       "0  0.30137   State-gov       Bachelors            0.8   Never-married   \n",
       "\n",
       "      occupation    relationship    race    sex  capital-gain  capital-loss  \\\n",
       "0   Adm-clerical   Not-in-family   White   Male       0.02174           0.0   \n",
       "\n",
       "   hours-per-week  native-country  \n",
       "0        0.397959   United-States  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入sklearn.preprocessing.StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 初始化一个 scaler，并将它施加到特征上\n",
    "scaler = MinMaxScaler()\n",
    "numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "features_raw[numerical] = scaler.fit_transform(data[numerical])\n",
    "\n",
    "# 显示一个经过缩放的样例记录\n",
    "display(features_raw.head(n = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：数据预处理\n",
    "\n",
    "从上面的**数据探索**中的表中，我们可以看到有几个属性的每一条记录都是非数字的。通常情况下，学习算法期望输入是数字的，这要求非数字的特征（称为类别变量）被转换。转换类别变量的一种流行的方法是使用**独热编码**方案。独热编码为每一个非数字特征的每一个可能的类别创建一个_“虚拟”_变量。例如，假设`someFeature`有三个可能的取值`A`，`B`或者`C`，。我们将把这个特征编码成`someFeature_A`, `someFeature_B`和`someFeature_C`.\n",
    "\n",
    "|   | 一些特征 |                    | 特征_A | 特征_B | 特征_C |\n",
    "| :-: | :-: |                            | :-: | :-: | :-: |\n",
    "| 0 |  B  |  | 0 | 1 | 0 |\n",
    "| 1 |  C  | ----> 独热编码 ----> | 0 | 0 | 1 |\n",
    "| 2 |  A  |  | 1 | 0 | 0 |\n",
    "\n",
    "此外，对于非数字的特征，我们需要将非数字的标签`'income'`转换成数值以保证学习算法能够正常工作。因为这个标签只有两种可能的类别（\"<=50K\"和\">50K\"），我们不必要使用独热编码，可以直接将他们编码分别成两个类`0`和`1`，在下面的代码单元中你将实现以下功能：\n",
    " - 使用[`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies)对`'features_raw'`数据来施加一个独热编码。\n",
    " - 将目标标签`'income_raw'`转换成数字项。\n",
    "   - 将\"<=50K\"转换成`0`；将\">50K\"转换成`1`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 total features after one-hot encoding.\n",
      "['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week', 'workclass_ Federal-gov', 'workclass_ Local-gov', 'workclass_ Private', 'workclass_ Self-emp-inc', 'workclass_ Self-emp-not-inc', 'workclass_ State-gov', 'workclass_ Without-pay', 'education_level_ 10th', 'education_level_ 11th', 'education_level_ 12th', 'education_level_ 1st-4th', 'education_level_ 5th-6th', 'education_level_ 7th-8th', 'education_level_ 9th', 'education_level_ Assoc-acdm', 'education_level_ Assoc-voc', 'education_level_ Bachelors', 'education_level_ Doctorate', 'education_level_ HS-grad', 'education_level_ Masters', 'education_level_ Preschool', 'education_level_ Prof-school', 'education_level_ Some-college', 'marital-status_ Divorced', 'marital-status_ Married-AF-spouse', 'marital-status_ Married-civ-spouse', 'marital-status_ Married-spouse-absent', 'marital-status_ Never-married', 'marital-status_ Separated', 'marital-status_ Widowed', 'occupation_ Adm-clerical', 'occupation_ Armed-Forces', 'occupation_ Craft-repair', 'occupation_ Exec-managerial', 'occupation_ Farming-fishing', 'occupation_ Handlers-cleaners', 'occupation_ Machine-op-inspct', 'occupation_ Other-service', 'occupation_ Priv-house-serv', 'occupation_ Prof-specialty', 'occupation_ Protective-serv', 'occupation_ Sales', 'occupation_ Tech-support', 'occupation_ Transport-moving', 'relationship_ Husband', 'relationship_ Not-in-family', 'relationship_ Other-relative', 'relationship_ Own-child', 'relationship_ Unmarried', 'relationship_ Wife', 'race_ Amer-Indian-Eskimo', 'race_ Asian-Pac-Islander', 'race_ Black', 'race_ Other', 'race_ White', 'sex_ Female', 'sex_ Male', 'native-country_ Cambodia', 'native-country_ Canada', 'native-country_ China', 'native-country_ Columbia', 'native-country_ Cuba', 'native-country_ Dominican-Republic', 'native-country_ Ecuador', 'native-country_ El-Salvador', 'native-country_ England', 'native-country_ France', 'native-country_ Germany', 'native-country_ Greece', 'native-country_ Guatemala', 'native-country_ Haiti', 'native-country_ Holand-Netherlands', 'native-country_ Honduras', 'native-country_ Hong', 'native-country_ Hungary', 'native-country_ India', 'native-country_ Iran', 'native-country_ Ireland', 'native-country_ Italy', 'native-country_ Jamaica', 'native-country_ Japan', 'native-country_ Laos', 'native-country_ Mexico', 'native-country_ Nicaragua', 'native-country_ Outlying-US(Guam-USVI-etc)', 'native-country_ Peru', 'native-country_ Philippines', 'native-country_ Poland', 'native-country_ Portugal', 'native-country_ Puerto-Rico', 'native-country_ Scotland', 'native-country_ South', 'native-country_ Taiwan', 'native-country_ Thailand', 'native-country_ Trinadad&Tobago', 'native-country_ United-States', 'native-country_ Vietnam', 'native-country_ Yugoslavia']\n"
     ]
    }
   ],
   "source": [
    "# TODO：使用pandas.get_dummies()对'features_raw'数据进行独热编码\n",
    "features = pd.get_dummies(features_raw)\n",
    "\n",
    "# TODO：将'income_raw'编码成数字值\n",
    "income = income_raw.replace(['>50K', '<=50K'], [1, 0])\n",
    "# 打印经过独热编码之后的特征数量\n",
    "encoded = list(features.columns)\n",
    "print \"{} total features after one-hot encoding.\".format(len(encoded))\n",
    "#print income.unique()\n",
    "# 移除下面一行的注释以观察编码的特征名字\n",
    "print encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 混洗和切分数据\n",
    "现在所有的 _类别变量_ 已被转换成数值特征，而且所有的数值特征已被规一化。和我们一般情况下做的一样，我们现在将数据（包括特征和它们的标签）切分成训练和测试集。其中80%的数据将用于训练和20%的数据用于测试。\n",
    "\n",
    "运行下面的代码单元来完成切分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 36177 samples.\n",
      "Testing set has 9045 samples.\n"
     ]
    }
   ],
   "source": [
    "# 导入 train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 将'features'和'income'数据切分成训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, income, test_size = 0.2, random_state = 0)\n",
    "\n",
    "# 显示切分的结果\n",
    "print \"Training set has {} samples.\".format(X_train.shape[0])\n",
    "print \"Testing set has {} samples.\".format(X_test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## 评价模型性能\n",
    "在这一部分中，我们将尝试四种不同的算法，并确定哪一个能够最好地建模数据。这里面的三个将是你选择的监督学习器，而第四种算法被称为一个*朴素的预测器*。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 评价方法和朴素的预测器\n",
    "*CharityML*通过他们的研究人员知道被调查者的年收入大于\\$50,000最有可能向他们捐款。因为这个原因*CharityML*对于准确预测谁能够获得\\$50,000以上收入尤其有兴趣。这样看起来使用**准确率**作为评价模型的标准是合适的。另外，把*没有*收入大于\\$50,000的人识别成年收入大于\\$50,000对于*CharityML*来说是有害的，因为他想要找到的是有意愿捐款的用户。这样，我们期望的模型具有准确预测那些能够年收入大于\\$50,000的能力比模型去**查全**这些被调查者*更重要*。我们能够使用**F-beta score**作为评价指标，这样能够同时考虑查准率和查全率：\n",
    "\n",
    "$$ F_{\\beta} = (1 + \\beta^2) \\cdot \\frac{precision \\cdot recall}{\\left( \\beta^2 \\cdot precision \\right) + recall} $$\n",
    "\n",
    "\n",
    "尤其是，当$\\beta = 0.5$的时候更多的强调查准率，这叫做**F$_{0.5}$ score** （或者为了简单叫做F-score）。\n",
    "\n",
    "通过查看不同类别的数据分布（那些最多赚\\$50,000和那些能够赚更多的），我们能发现：很明显的是很多的被调查者年收入没有超过\\$50,000。这点会显著地影响**准确率**，因为我们可以简单地预测说*“这个人的收入没有超过\\$50,000”*，这样我们甚至不用看数据就能做到我们的预测在一般情况下是正确的！做这样一个预测被称作是**朴素的**，因为我们没有任何信息去证实这种说法。通常考虑对你的数据使用一个*朴素的预测器*是十分重要的，这样能够帮助我们建立一个模型的表现是否好的基准。那有人说，使用这样一个预测是没有意义的：如果我们预测所有人的收入都低于\\$50,000，那么*CharityML*就不会有人捐款了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 1 - 朴素预测器的性能\n",
    "*如果我们选择一个无论什么情况都预测被调查者年收入大于\\$50,000的模型，那么这个模型在这个数据集上的准确率和F-score是多少？*  \n",
    "**注意：** 你必须使用下面的代码单元将你的计算结果赋值给`'accuracy'` 和 `'fscore'`，这些值会在后面被使用，请注意这里不能使用scikit-learn，你需要根据公式自己实现相关计算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]\n"
     ]
    }
   ],
   "source": [
    "# TODO： 计算准确率\n",
    "accuracy = np.divide(n_greater_50k, float(n_records))\n",
    "\n",
    "# TODO： 使用上面的公式，并设置beta=0.5计算F-score\n",
    "recall = np.divide(n_greater_50k, n_greater_50k)\n",
    "fscore = (1 + np.power(0.5, 2)) * np.multiply(accuracy, recall) / (np.power(0.5, 2) * accuracy + recall)\n",
    "\n",
    "# 打印结果\n",
    "print \"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy, fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 监督学习模型\n",
    "**下面的监督学习模型是现在在** [`scikit-learn`](http://scikit-learn.org/stable/supervised_learning.html) **中你能够选择的模型**\n",
    "- 高斯朴素贝叶斯 (GaussianNB)\n",
    "- 决策树\n",
    "- 集成方法 (Bagging, AdaBoost, Random Forest, Gradient Boosting)\n",
    "- K近邻 (KNeighbors)\n",
    "- 随机梯度下降分类器 (SGDC)\n",
    "- 支撑向量机 (SVM)\n",
    "- Logistic回归\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 2 - 模型应用\n",
    "\n",
    "列出从上面的监督学习模型中选择的三个适合我们这个问题的模型，你将在人口普查数据上测试这每个算法。对于你选择的每一个算法：\n",
    "\n",
    "- *描述一个该模型在真实世界的一个应用场景。（你需要为此做点研究，并给出你的引用出处）*\n",
    "- *这个模型的优势是什么？他什么情况下表现最好？*\n",
    "- *这个模型的缺点是什么？什么条件下它表现很差？*\n",
    "- *根据我们当前数据集的特点，为什么这个模型适合这个问题。*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答： **选择的算法决策树、集成方法、支撑向量机（SVM）<br/>\n",
    "* 决策树<br/>\n",
    "  * 应用场景：银行等信用机构预测贷款用户是否有偿还贷款的能力。[引用出处](http://blog.jobbole.com/89072/)<br/>\n",
    "  * 优势：决策树易于理解和实现，对缺失值不敏感，模型可以一次构建反复使用。什么情况表现好：实例是由“属性-值”对表示，目标函数具有离散的输出值。<br/>\n",
    "  * 缺点：容易出现过拟合，对于类比不一致样本，决策树的信心增益倾向于那些数据值较多的特征。什么条件下表现差：分类的类别过于复杂，数据的属性之间具有很强的关联性。<br/>\n",
    "  * 为什么适合：该问题是非线性的，决策树可以很好的解决非线性问题，而且不需要对数据过多的处理等。<br>\n",
    "* 集成方法（AdaBoost）<br/>\n",
    "  * 应用场景：模式识别，计算机视觉领域等。[引用出处](http://blog.csdn.net/mach_learn/article/details/39501849)<br/>\n",
    "  * 优势：很好的利用了弱分类器进行级联，具有很高的精度。什么情况表现好：解决二分类和多分类问题，。\n",
    "  * 缺点：数据集的不平衡分布导致分类器的分类精度下降，训练比较耗费时间。什么条件下表现差：数据集分布很不均匀。\n",
    "  * 为什么适合：income只有两个属性，因此可以归为二分类问题，适用于AdaBoost。\n",
    "* 支持向量机（SVM）\n",
    "  * 应用场景：模式识别相关领域。[引用出处](http://blog.csdn.net/gdut2015go/article/details/46711837)\n",
    "  * 优势：可以有效处理高维空间数据。什么情况表现好：数据的维度较高\n",
    "  * 缺点：无法处理大规模的数据集，训练时间较长。什么条件下表现差：数据集数据量大，对算法的训练效率要求较高。\n",
    "  * 为什么适合：该项目数据集经过独热编码后维度较高，而且该项目提供的样本数据相对较少。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习 - 创建一个训练和预测的流水线\n",
    "为了正确评估你选择的每一个模型的性能，创建一个能够帮助你快速有效地使用不同大小的训练集并在测试集上做预测的训练和测试的流水线是十分重要的。\n",
    "你在这里实现的功能将会在接下来的部分中被用到。在下面的代码单元中，你将实现以下功能：\n",
    "\n",
    " - 从[`sklearn.metrics`](http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics)中导入`fbeta_score`和`accuracy_score`。\n",
    " - 用样例训练集拟合学习器，并记录训练时间。\n",
    " - 用学习器来对训练集进行预测并记录预测时间。\n",
    " - 在最前面的300个*训练数据*上做预测。\n",
    " - 计算训练数据和测试数据的准确率。\n",
    " - 计算训练数据和测试数据的F-score。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO：从sklearn中导入两个评价指标 - fbeta_score和accuracy_score\n",
    "from sklearn.metrics import fbeta_score, accuracy_score\n",
    "\n",
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "    '''\n",
    "    inputs:\n",
    "       - learner: the learning algorithm to be trained and predicted on\n",
    "       - sample_size: the size of samples (number) to be drawn from training set\n",
    "       - X_train: features training set\n",
    "       - y_train: income training set\n",
    "       - X_test: features testing set\n",
    "       - y_test: income testing set\n",
    "    '''\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    # TODO：使用sample_size大小的训练数据来拟合学习器\n",
    "    # TODO: Fit the learner to the training data using slicing with 'sample_size'\n",
    "    start = time() # 获得程序开始时间\n",
    "    learner = learner.fit(X_train[:sample_size],y_train[:sample_size])\n",
    "    end = time() # 获得程序结束时间\n",
    "    \n",
    "    # TODO：计算训练时间\n",
    "    results['train_time'] = end - start\n",
    "    \n",
    "    # TODO: 得到在测试集上的预测值\n",
    "    #       然后得到对前300个训练数据的预测结果\n",
    "    start = time() # 获得程序开始时间\n",
    "    predictions_test = learner.predict(X_test)\n",
    "    predictions_train = learner.predict(X_train[: 300])\n",
    "    end = time() # 获得程序结束时间\n",
    "    \n",
    "    # TODO：计算预测用时\n",
    "    results['pred_time'] = end - start\n",
    "            \n",
    "    # TODO：计算在最前面的300个训练数据的准确率\n",
    "    results['acc_train'] = accuracy_score(y_train[: 300], predictions_train)\n",
    "        \n",
    "    # TODO：计算在测试集上的准确率\n",
    "    results['acc_test'] = accuracy_score(y_test,predictions_test)\n",
    "    \n",
    "    # TODO：计算在最前面300个训练数据上的F-score\n",
    "    results['f_train'] = fbeta_score(y_train[: 300], predictions_train, beta = 0.5)\n",
    "        \n",
    "    # TODO：计算测试集上的F-score\n",
    "    results['f_test'] = fbeta_score(y_test, predictions_test,beta = 0.5)\n",
    "       \n",
    "    # 成功\n",
    "    print \"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size)  \n",
    "    #print y_train.unique()\n",
    "    # 返回结果\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：初始模型的评估\n",
    "在下面的代码单元中，您将需要实现以下功能：             \n",
    "- 导入你在前面讨论的三个监督学习模型。             \n",
    "- 初始化三个模型并存储在`'clf_A'`，`'clf_B'`和`'clf_C'`中。         \n",
    "  - 如果可能对每一个模型都设置一个`random_state`。       \n",
    "  - **注意：**这里先使用每一个模型的默认参数，在接下来的部分中你将需要对某一个模型的参数进行调整。             \n",
    "- 计算记录的数目等于1%，10%，和100%的训练数据，并将这些值存储在`'samples'`中             \n",
    "\n",
    "**注意：**取决于你选择的算法，下面实现的代码可能需要一些时间来运行！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[361, 3617, 36177]\n",
      "DecisionTreeClassifier trained on 361 samples.\n",
      "DecisionTreeClassifier trained on 3617 samples.\n",
      "DecisionTreeClassifier trained on 36177 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/liuchunlei/anaconda2/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC trained on 361 samples.\n",
      "SVC trained on 3617 samples.\n",
      "SVC trained on 36177 samples.\n",
      "AdaBoostClassifier trained on 361 samples.\n",
      "AdaBoostClassifier trained on 3617 samples.\n",
      "AdaBoostClassifier trained on 36177 samples.\n"
     ]
    },
    {
     "data": {
      "image/png": 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ExVkKwEtCiN5qivstAGuJyAbS9jYWcgSsPmTn8HNzE1Mdo05ZhLlLRO8CmENEXpAvsTtK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bC6uUEaYn0pIJJsVFW/6LZ/VV2SZIdxzQuBfdvHy2hOjbKAkEbD33U8fgi4qapWdrHepcBOSebT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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcfb8fdca10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO：从sklearn中导入三个监督学习模型\n",
    "#from sklearn import tree,svm,ensemble\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "# TODO：初始化三个模型\n",
    "clf_A = DecisionTreeClassifier(random_state = 0)\n",
    "clf_B = SVC(random_state = 0)\n",
    "clf_C = AdaBoostClassifier(random_state = 0)\n",
    "\n",
    "# TODO：计算1%， 10%， 100%的训练数据分别对应多少点\n",
    "samples_1 = int(X_train.shape[0]*0.01)\n",
    "samples_10 = int(X_train.shape[0]*0.1)\n",
    "samples_100 = int(X_train.shape[0]*1)\n",
    "print [samples_1,samples_10,samples_100]\n",
    "# 收集学习器的结果\n",
    "results = {}\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    results[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_1, samples_10, samples_100]):\n",
    "        results[clf_name][i] = train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
    "        \n",
    "\n",
    "# 对选择的三个模型得到的评价结果进行可视化\n",
    "vs.evaluate(results, accuracy, fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## 提高效果\n",
    "\n",
    "在这最后一节中，您将从三个有监督的学习模型中选择*最好的*模型来使用学生数据。你将在整个训练集（`X_train`和`y_train`）上通过使用网格搜索优化至少调节一个参数以获得一个比没有调节之前更好的F-score。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 3 - 选择最佳的模型\n",
    "\n",
    "*基于你前面做的评价，用一到两段向*CharityML*解释这三个模型中哪一个对于判断被调查者的年收入大于\\$50,000是最合适的。*             \n",
    "**提示：**你的答案应该包括关于评价指标，预测/训练时间，以及该算法是否适合这里的数据的讨论。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：**AdaBoost算法最合适。<br/>\n",
    "1. 在训练集上虽然决策树表现最好，但在测试集上几乎表现最差，说明出现了过拟合现象。AdaBoost与SVM相比，在训练和测试分数上都有优势。<br/>\n",
    "2. 在预测/训练时间上，AdaBoost和决策树相差不大，但SVM使用的时间太长，说明算法的效率比较差。<br/>\n",
    "3. 此数据集包含很多标签信息，适合使用AdaBoost来处理。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 4 - 用通俗的话解释模型\n",
    "\n",
    "*用一到两段话，向*CharityML*用外行也听得懂的话来解释最终模型是如何工作的。你需要解释所选模型的主要特点。例如，这个模型是怎样被训练的，它又是如何做出预测的。避免使用高级的数学或技术术语，不要使用公式或特定的算法名词。*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答： ** AdaBoost算法基本原理就是将多个弱分类器进行合理的结合，使其成为一个强分类器。首先对N个样本赋予初始的权值1/N，训练过程中如果某个样本点已经被准确的分类，那么构造下一个训练集时该数据的权值会被降低，相反，如果某个样本点没有被准确的分类，那么该数据的权值就会增加，然后将权值更新后的样本数据用于训练下一个分类器，如此迭代下去。各个分类器训练结束后，加大误差率小的分类器的权重，使该分类器最终的分类函数起到较大的作用，降低误差率大的分类器的权重，使其在最终的分类器占得权重较小。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：模型调优\n",
    "调节选择的模型的参数。使用网格搜索（GridSearchCV）来至少调整模型的重要参数（至少调整一个），这个参数至少需给出并尝试3个不同的值。你要使用整个训练集来完成这个过程。在接下来的代码单元中，你需要实现以下功能：\n",
    "\n",
    "- 导入[`sklearn.model_selection.GridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)和[`sklearn.metrics.make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html).\n",
    "- 初始化你选择的分类器，并将其存储在`clf`中。\n",
    " - 如果能够设置的话，设置`random_state`。\n",
    "- 创建一个对于这个模型你希望调整参数的字典。\n",
    " - 例如: parameters = {'parameter' : [list of values]}。\n",
    " - **注意：** 如果你的学习器（learner）有 `max_features` 参数，请不要调节它！\n",
    "- 使用`make_scorer`来创建一个`fbeta_score`评分对象（设置$\\beta = 0.5$）。\n",
    "- 在分类器clf上用'scorer'作为评价函数运行网格搜索，并将结果存储在grid_obj中。\n",
    "- 用训练集（X_train, y_train）训练grid search object,并将结果存储在`grid_fit`中。\n",
    "\n",
    "**注意：** 取决于你选择的参数列表，下面实现的代码可能需要花一些时间运行！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.8576\n",
      "F-score on testing data: 0.7246\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.8663\n",
      "Final F-score on the testing data: 0.7425\n"
     ]
    }
   ],
   "source": [
    "# TODO：导入'GridSearchCV', 'make_scorer'和其他一些需要的库\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.model_selection import GridSearchCV,KFold\n",
    "from sklearn.metrics import make_scorer\n",
    "# TODO：初始化分类器\n",
    "clf = AdaBoostClassifier(random_state = 0)\n",
    "\n",
    "# TODO：创建你希望调节的参数列表\n",
    "parameters = {'n_estimators':[100,200,300]}\n",
    "\n",
    "# TODO：创建一个fbeta_score打分对象\n",
    "scorer = make_scorer(fbeta_score, beta = 0.5)\n",
    "\n",
    "# TODO：在分类器上使用网格搜索，使用'scorer'作为评价函数\n",
    "grid_obj = GridSearchCV(clf, parameters, scorer, cv=KFold(n_splits= 10))\n",
    "\n",
    "# TODO：用训练数据拟合网格搜索对象并找到最佳参数\n",
    "grid_fit = grid_obj.fit(X_train, y_train)\n",
    "# 得到estimator\n",
    "best_clf = grid_obj.best_estimator_\n",
    "\n",
    "# 使用没有调优的模型做预测\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "best_predictions = best_clf.predict(X_test)\n",
    "\n",
    "# 汇报调参前和调参后的分数\n",
    "print \"Unoptimized model\\n------\"\n",
    "print \"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions))\n",
    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5))\n",
    "print \"\\nOptimized Model\\n------\"\n",
    "print \"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions))\n",
    "print \"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 5 - 最终模型评估\n",
    "\n",
    "_你的最优模型在测试数据上的准确率和F-score是多少？这些分数比没有优化的模型好还是差？你优化的结果相比于你在**问题 1**中得到的朴素预测器怎么样？_  \n",
    "**注意：**请在下面的表格中填写你的结果，然后在答案框中提供讨论。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结果:\n",
    "\n",
    "|     评价指标     | 基准预测器 | 未优化的模型 | 优化的模型 |\n",
    "| :------------: | :-----------------: | :---------------: | :-------------: | \n",
    "| 准确率 |       0.2478              |      0.8576             |   0.8663              |\n",
    "| F-score        |     0.2917               |     0.7246              |   0.7425      |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：**使用AdaBoost模型并通过网格搜索对参数n_estimators进行调整，得到的分数略微好于没有优化的模型，但远远好于朴素预测器得到的分数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## 特征的重要性\n",
    "\n",
    "在数据上（比如我们这里使用的人口普查的数据）使用监督学习算法的一个重要的任务是决定哪些特征能够提供最强的预测能力。通过专注于一些少量的有效特征和标签之间的关系，我们能够更加简单地理解这些现象，这在很多情况下都是十分有用的。在这个项目的情境下这表示我们希望选择一小部分特征，这些特征能够在预测被调查者是否年收入大于\\$50,000这个问题上有很强的预测能力。\n",
    "\n",
    "选择一个有`feature_importance_`属性（这是一个根据这个选择的分类器来对特征的重要性进行排序的函数）的scikit学习分类器（例如，AdaBoost，随机森林）。在下一个Python代码单元中用这个分类器拟合训练集数据并使用这个属性来决定这个人口普查数据中最重要的5个特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 6 - 观察特征相关性\n",
    "\n",
    "当**探索数据**的时候，它显示在这个人口普查数据集中每一条记录我们有十三个可用的特征。             \n",
    "_在这十三个记录中，你认为哪五个特征对于预测是最重要的，你会怎样对他们排序？理由是什么？_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：**education_level:因为受教育程度越高的人往往拥有获得更多收入的能力。<br/>\n",
    "hours-per-week:工作时间越多获得更多收入的可能越大。<br/>\n",
    "age:年龄越大经验越丰富，获得更多收入的机会越大。<br/>\n",
    "captial-gain:资金的获得多少是决定收入的重要部分。<br/>\n",
    "occupation:虽然各行各业都有收入多和收入少的，但有时候你所在的行业决定了你收入的水平。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习 - 提取特征重要性\n",
    "\n",
    "选择一个`scikit-learn`中有`feature_importance_`属性的监督学习分类器，这个属性是一个在做预测的时候根据所选择的算法来对特征重要性进行排序的功能。\n",
    "\n",
    "在下面的代码单元中，你将要实现以下功能：\n",
    " - 如果这个模型和你前面使用的三个模型不一样的话从sklearn中导入一个监督学习模型。\n",
    " - 在整个训练集上训练一个监督学习模型。\n",
    " - 使用模型中的`'.feature_importances_'`提取特征的重要性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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PHky/fv1o0KABACeccAJPPPEETZo02aJlt2rVikaNGlFQUADAAw88UPyu34q4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\n/wfaUXA4sbHpoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa764cb790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO：导入一个有'feature_importances_'的监督学习模型\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "# TODO：在训练集上训练一个监督学习模型\n",
    "model = ExtraTreesClassifier(random_state = 0)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# TODO： 提取特征重要性\n",
    "importances = model.feature_importances_\n",
    "# 绘图\n",
    "vs.feature_plot(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 7 - 提取特征重要性\n",
    "观察上面创建的展示五个用于预测被调查者年收入是否大于\\$50,000最相关的特征的可视化图像。\n",
    "_这五个特征和你在**问题 6**中讨论的特征比较怎么样？如果说你的答案和这里的相近，那么这个可视化怎样佐证了你的想法？如果你的选择不相近，那么为什么你觉得这些特征更加相关？_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：**与问题6中讨论的部分相关，例如：age hours-per-week,  matrial-status_Married-civ-spouse表示已婚，稳定的婚姻也是保证收入的重要条件，relationship_Husband表示已婚，家庭责任也是促进一个人努力工作增加收入的重要因素。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征选择\n",
    "\n",
    "如果我们只是用可用特征的一个子集的话模型表现会怎么样？通过使用更少的特征来训练，在评价指标的角度来看我们的期望是训练和预测的时间会更少。从上面的可视化来看，我们可以看到前五个最重要的特征贡献了数据中**所有**特征中超过一半的重要性。这提示我们可以尝试去*减小特征空间*，并简化模型需要学习的信息。下面代码单元将使用你前面发现的优化模型，并*只使用五个最重要的特征*在相同的训练集上训练模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8663\n",
      "F-score on testing data: 0.7425\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8226\n",
      "F-score on testing data: 0.6396\n"
     ]
    }
   ],
   "source": [
    "# 导入克隆模型的功能\n",
    "from sklearn.base import clone\n",
    "\n",
    "# 减小特征空间\n",
    "X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "\n",
    "# 在前面的网格搜索的基础上训练一个“最好的”模型\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "\n",
    "# 做一个新的预测\n",
    "reduced_predictions = clf.predict(X_test_reduced)\n",
    "\n",
    "# 对于每一个版本的数据汇报最终模型的分数\n",
    "print \"Final Model trained on full data\\n------\"\n",
    "print \"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions))\n",
    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5))\n",
    "print \"\\nFinal Model trained on reduced data\\n------\"\n",
    "print \"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, reduced_predictions))\n",
    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, reduced_predictions, beta = 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 8 - 特征选择的影响\n",
    "\n",
    "*最终模型在只是用五个特征的数据上和使用所有的特征数据上的F-score和准确率相比怎么样？*  \n",
    "*如果训练时间是一个要考虑的因素，你会考虑使用部分特征的数据作为你的训练集吗？*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：**如果只是用五个特种数据会使得F-score和准确率略有下降。如果需要考虑训练时间，我会考虑只使用一部分比较重要的特征作为训练集。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **注意：** 当你写完了所有的代码，并且回答了所有的问题。你就可以把你的 iPython Notebook 导出成 HTML 文件。你可以在菜单栏，这样导出**File -> Download as -> HTML (.html)**把这个 HTML 和这个 iPython notebook 一起做为你的作业提交。"
   ]
  }
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