titanicNord.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "本文作者大树先生，首发于作者的知乎专栏《机器学习之路》\n",
    "Kaggle上的入门比赛：Titanic: Machine Learning from Disaster。\n",
    "    \n",
    "https://www.kaggle.com/c/titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "1. 数据总览\n",
    "Titanic 生存模型预测，其中包含了两组数据：train.csv 和 test.csv，分别为训练集合和测试集合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv('./input/train.csv')\n",
    "test_data = pd.read_csv('./input/test.csv')\n",
    "sns.set_style('whitegrid')\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<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"
     ]
    }
   ],
   "source": [
    "train_data.info() #数据信息总览：\n",
    "print(\"-\" * 40)\n",
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "从上面我们可以看出，Age、Cabin、Embarked几个特征存在缺失值。\n",
    "\n",
    "绘制存活的比例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9da2a2f278>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+0HMdV1bMwNEQGbv8ibRZfCpc+wL0udR0krCisg6iI3XN3PbqGj7effLindq5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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9da2ae1e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['Survived'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-7-921ca0efd1f0>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-7-921ca0efd1f0>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    2. 缺失值处理的方法\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    " 2. 缺失值处理的方法\n",
    "对数据进行分析的时候要注意其中是否有缺失值。\n",
    "\n",
    "一些机器学习算法能够处理缺失值，比如神经网络，一些则不能。对于缺失值，一般有以下几种处理方法：\n",
    "\n",
    "（1）如果数据集很多，但有很少的缺失值，可以删掉带缺失值的行；\n",
    "\n",
    "（2）如果该属性相对学习来说不是很重要，可以对缺失值赋均值或者众数。\n",
    "比如在哪儿上船Embarked这一属性（共有三个上船地点），缺失俩值，可以用众数赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data.Embarked[train_data.Embarked.isnull()] = train_data.Embarked.dropna().mode().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#（3）对于标称属性，可以赋一个代表缺失的值，比如‘U0’。因为缺失本身也可能代表着一些隐含信息。比如船舱号Cabin这一属性，缺失可能代表并没有船舱。\n",
    "#replace missing value with U0\n",
    "train_data['Cabin'] = train_data.Cabin.fillna('U0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "（4）使用回归 随机森林等模型来预测缺失属性的值。\n",
    "因为Age在该数据集里是一个相当重要的特征（先对Age进行分析即可得知），\n",
    "所以保证一定的缺失值填充准确率是非常重要的，对结果也会产生较大影响。\n",
    "一般情况下，会使用数据完整的条目作为模型的训练集，以此来预测缺失值。\n",
    "对于当前的这个数据，可以使用随机森林来预测也可以使用线性回归预测。\n",
    "这里使用随机森林预测模型，选取数据集中的数值属性作为特征\n",
    "（因为sklearn的模型只能处理数值属性，所以这里先仅选取数值特征，但在实际的应用中需要将非数值特征转换为数值特征）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor#choose training data to predict age\n",
    "age_df = train_data[['Age','Survived','Fare', 'Parch', 'SibSp', 'Pclass']]\n",
    "age_df_notnull = age_df.loc[(train_data['Age'].notnull())]\n",
    "age_df_isnull = age_df.loc[(train_data['Age'].isnull())]\n",
    "X = age_df_notnull.values[:,1:]\n",
    "Y = age_df_notnull.values[:,0]\n",
    "# use RandomForestRegression to train data\n",
    "RFR = RandomForestRegressor(n_estimators=1000, n_jobs=-1)\n",
    "RFR.fit(X,Y)\n",
    "predictAges = RFR.predict(age_df_isnull.values[:,1:])\n",
    "train_data.loc[train_data['Age'].isnull(), ['Age']]= predictAges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<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            891 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          891 non-null object\n",
      "Embarked       891 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#3. 分析数据关系\n",
    "#(1) 性别与是否生存的关系 Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Survived\n",
       "female  0            81\n",
       "        1           233\n",
       "male    0           468\n",
       "        1           109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Sex','Survived'])['Survived'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b46f76c88>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QTIBPB/bVtPuA+cf7AyuVyvG+VQXyfGs8K/3z2UyAT2mwbfB4f2BnZ+fxvnWM9La7gAll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\nFcoAl6RCGeCSVCgDXJIK9f8Borh9UZPmyZwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3f0eab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Sex','Survived']].groupby(['Sex']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#以上为不同性别的生存率，可见在泰坦尼克号事故中，还是体现了Lady First。\n",
    "#(2) 船舱等级和生存与否的关系 Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Survived\n",
       "1       0            80\n",
       "        1           136\n",
       "2       0            97\n",
       "        1            87\n",
       "3       0           372\n",
       "        1           119\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Pclass','Survived'])['Pclass'].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b3843c2e8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b38357cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Pclass','Survived']].groupby(['Pclass']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b3c41e5c0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6fZ2ZHZ5+fwzJkM+RwOVmNjjTn4B0ewpwCVJ6vcVZwI/N7CWSI+FS04BngceA\nFUX3X2tmS81sGfAUyfj2GcCydKjmYGCOmfUAPg68SXKpt2+a2Yp0mzPYNu5+NnBeWsdyYKKZ7QT8\nB3Cuu79GMgb+3fQvB5FMaCm9SCvMbDhJAF+Wdy0i5SjARUQCpSEUEZFAKcBFRAKlABcRCZQCXEQk\nUApwEZFAKcBFRAKlABcRCdT/A6pVLqrOAzw5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3c3bd470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Sex','Pclass','Survived']].groupby(['Pclass','Sex']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Pclass  Survived\n",
       "female  1       0             3\n",
       "                1            91\n",
       "        2       0             6\n",
       "                1            70\n",
       "        3       0            72\n",
       "                1            72\n",
       "male    1       0            77\n",
       "                1            45\n",
       "        2       0            91\n",
       "                1            17\n",
       "        3       0           300\n",
       "                1            47\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#不同等级船舱的男女生存率：\n",
    "\n",
    "train_data.groupby(['Sex', 'Pclass', 'Survived'])['Survived'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "从图和表中可以看出，总体上泰坦尼克号逃生是妇女优先，但是对于不同等级的船舱还是有一定的区别。\n",
    "\n",
    "(3) 年龄与存活与否的关系 Age\n",
    "\n",
    "分别分析不同等级船舱和不同性别下的年龄分布和生存的关系："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oc9TU1Pge52S2evVqIISTWwDRbgC2b99OZ2dnsIGJiIxQs1c0t6WlRZ/54ovx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\nnWm2AzuOmvDRWR3S0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RIENzRHvhoiDwTIHK18IC7XoUMHzJs3D1JFNsTybNZxVKPBBtQ0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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3830b208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize = (18, 8))\n",
    "sns.violinplot(\"Pclass\", \"Age\", hue=\"Survived\", data=train_data, split=True, ax=ax[0])\n",
    "ax[0].set_title('Pclass and Age vs Survived')\n",
    "ax[0].set_yticks(range(0, 110, 10))\n",
    "sns.violinplot(\"Sex\", \"Age\", hue=\"Survived\", data=train_data, split=True, ax=ax[1])\n",
    "ax[1].set_title('Sex and Age vs Survived')\n",
    "ax[1].set_yticks(range(0, 110, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "分析总体的年龄分布："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1w0okSdKgaiwoR8QLgY8Avwg8BXwoM6+NiHXAx4EJYC/whsx8pKk6JEmSpMPRZNeLg8C7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3c392f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "train_data['Age'].hist(bins=70)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Num')\n",
    "plt.subplot(122)\n",
    "train_data.boxplot(column='Age', showfliers=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "不同年龄下的生存和非生存的分布情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1b3831a7f0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0Ly4kKPO/0OF2h76RfjxxaV8Gxwbwz2/3Mu3/1pFdVmfHqIUQQghxukjxKIQ9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\nhBBCCLEuCY9CCCGEEEIIIdb1/wGyWNg+1TjkOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3c3b4f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train_data, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train_data['Age'].max()))\n",
    "facet.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "不同年龄下的平均生存率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b3bc43dd8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B7jlU38J18GgbdwvVHp11JrfzXTXTlOuqmaZcV8005bpqpinXVTONOdprpi17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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3beecc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# average survived passengers by agefig, \n",
    "axis1 = plt.subplots(1,1,figsize=(18,4))\n",
    "train_data[\"Age_int\"] = train_data[\"Age\"].astype(int)\n",
    "average_age = train_data[[\"Age_int\", \"Survived\"]].groupby(['Age_int'],as_index=False).mean()\n",
    "sns.barplot(x='Age_int', y='Survived', data=average_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      29.661614\n",
       "std       13.737557\n",
       "min        0.420000\n",
       "25%       21.000000\n",
       "50%       28.000000\n",
       "75%       37.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "样本有891，平均年龄约为30岁，标准差13.5岁，最小年龄为0.42，最大年龄80.\n",
    "\n",
    "按照年龄，将乘客划分为儿童、少年、成年和老年，分析四个群体的生还情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group\n",
       "(0, 12]      0.506173\n",
       "(12, 18]     0.466667\n",
       "(18, 65]     0.364512\n",
       "(65, 100]    0.125000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 12, 18, 65, 100]\n",
    "train_data['Age_group'] = pd.cut(train_data['Age'], bins)\n",
    "by_age = train_data.groupby('Age_group')['Survived'].mean()\n",
    "by_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b3bb23780>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WyhDw7G1ZywC4AHhqv4sozGrgRcDdEfF24OXAKuDUiDgsM100rr7PUE0ffDfV0AlUS2Of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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3bbdaf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_age.plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(4) 称呼与存活与否的关系 Name\n",
    "\n",
    "通过观察名字数据，我们可以看出其中包括对乘客的称呼，如：Mr、Miss、Mrs等，\n",
    "称呼信息包含了乘客的年龄、性别，同时也包含了如社会地位等的称呼，如：Dr,、Lady、Major、Master等的称呼。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>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>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Title'] = train_data['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "pd.crosstab(train_data['Title'], train_data['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "观察不同称呼与生存率的关系："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b3c03c470>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tSbqSsuhOR1H8wcxmk1yjNdNZ4VK5s+CdRB1nwQuddEN2KzSwT71uofusG2Af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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3c196828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Title','Survived']].groupby(['Title']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "同时，对于名字，我们还可以观察名字长度和生存率之间存在关系的可能："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b3bf3deb8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MYV7bOTIo71bgV5n5nd79E82RiNgNeBpNY2/ja6TV/BiU16u71fwYkddqfsyT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\nqs6GgyRJkiRJqu7/A252+s06UnVfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3bf33be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis1 = plt.subplots(1,1,figsize=(18,4))\n",
    "train_data['Name_length'] = train_data['Name'].apply(len)\n",
    "name_length = train_data[['Name_length','Survived']].groupby(['Name_length'],as_index=False).mean()\n",
    "sns.barplot(x='Name_length', y='Survived', data=name_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "从上面的图片可以看出，名字长度和生存与否确实也存在一定的相关性。\n",
    "\n",
    "(5) 有无兄弟姐妹和存活与否的关系 SibSp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将数据分为有兄弟姐妹的和没有兄弟姐妹的两组：\n",
    "sibsp_df = train_data[train_data['SibSp'] != 0]\n",
    "no_sibsp_df = train_data[train_data['SibSp'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W2o+Aq4wxTwHnAb8EVChVqbVZMPew6KvvjoqMXbpLZPKQFhYbz2M717mkLOq6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\nREREpAAVSiIiIiIFqFASERERKeD/AZMbpBawPVqnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3b59da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "sibsp_df['Survived'].value_counts().plot.pie(labels=['No Survived', 'Survived'], autopct = '%1.1f%%')\n",
    "plt.xlabel('sibsp')\n",
    "plt.subplot(122)\n",
    "no_sibsp_df['Survived'].value_counts().plot.pie(labels=['No Survived', 'Survived'], autopct = '%1.1f%%')\n",
    "plt.xlabel('no_sibsp')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(6) 有无父母子女和存活与否的关系 Parch\n",
    "\n",
    "和有无兄弟姐妹一样，同样分析可以得到："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w8ACWDGuivSUIaMHN6ryS7/SyQuqy9SLixy8VShFzedN5zzUGHTv5ziHL8X78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\nLnnghPB3VaSrc4DXfIfoj4M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3qQL+jiuCzuxhm8m4y3I7d3rscOAvxphf4gYY3YTr+P1D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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3b46f198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parch_df = train_data[train_data['Parch'] != 0]\n",
    "no_parch_df = train_data[train_data['Parch'] == 0]\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "parch_df['Survived'].value_counts().plot.pie(labels=['No Survived', 'Survived'], autopct = '%1.1f%%')\n",
    "plt.xlabel('parch')\n",
    "plt.subplot(122)\n",
    "no_parch_df['Survived'].value_counts().plot.pie(labels=['No Survived', 'Survived'], autopct = '%1.1f%%')\n",
    "plt.xlabel('no_parch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(7) 亲友的人数和存活与否的关系 SibSp & Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f1b3b432208>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ILd09N9PCWFGTn4PDkryvu78wb/2J3f322VW2sqrquCS7u/vqybPP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\n5fgorncVfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3b466f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "train_data[['Parch','Survived']].groupby(['Parch']).mean().plot.bar(ax=ax[0])\n",
    "ax[0].set_title('Parch and Survived')\n",
    "train_data[['SibSp','Survived']].groupby(['SibSp']).mean().plot.bar(ax=ax[1])\n",
    "ax[1].set_title('SibSp and Survived')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b3b452fd0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3bf33ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['Family_Size'] = train_data['Parch'] + train_data['SibSp'] + 1\n",
    "train_data[['Family_Size','Survived']].groupby(['Family_Size']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "从图表中可以看出，若独自一人，那么其存活率比较低；但是如果亲友太多的话，存活率也会很低。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(8) 票价分布和存活与否的关系 Fare\n",
    "\n",
    "首先绘制票价的分布情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3b373320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3b249a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "train_data['Fare'].hist(bins = 70)\n",
    "train_data.boxplot(column='Fare', by='Pclass', showfliers=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      32.204208\n",
       "std       49.693429\n",
       "min        0.000000\n",
       "25%        7.910400\n",
       "50%       14.454200\n",
       "75%       31.000000\n",
       "max      512.329200\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Fare'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "绘制生存与否与票价均值和方差的关系："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6JBXEUJekghjqklSQ/wNoc3Mm6DulTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3b1fdf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fare_not_survived = train_data['Fare'][train_data['Survived'] == 0]\n",
    "fare_survived = train_data['Fare'][train_data['Survived'] == 1]\n",
    "average_fare = pd.DataFrame([fare_not_survived.mean(), fare_survived.mean()])\n",
    "std_fare = pd.DataFrame([fare_not_survived.std(), fare_survived.std()])\n",
    "average_fare.plot(yerr=std_fare, kind='bar', legend=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "由上图标可知，票价与是否生还有一定的相关性，生还者的平均票价要大于未生还者的平均票价。\n",
    "\n",
    "(9) 船舱类型和存活与否的关系 Cabin\n",
    "\n",
    "由于船舱的缺失值确实太多，有效值仅仅有204个，很难分析出不同的船舱和存活的关系，所以在做特征工程的时候，可以直接将该组特征丢弃。\n",
    "\n",
    "当然，这里我们也可以对其进行一下分析，对于缺失的数据都分为一类。\n",
    "\n",
    "简单地将数据分为是否有Cabin记录作为特征，与生存与否进行分析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b3af299e8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gdlN7FrD9KdYkSXqKqszAtwBzI2IOcC+wDFhxQKuSJE1owhl4Zu4GBoDNQB24OjOHImJ1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\nQhngklSo/weVBQJaKXABvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3afe08d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replace missing values with \"U0\"\n",
    "train_data.loc[train_data.Cabin.isnull(), 'Cabin'] = 'U0'\n",
    "train_data['Has_Cabin'] = train_data['Cabin'].apply(lambda x: 0 if x == 'U0' else 1)\n",
    "train_data[['Has_Cabin','Survived']].groupby(['Has_Cabin']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "对不同类型的船舱进行分析：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b3af7c4a8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3af488d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create feature for the alphabetical part of the cabin number\n",
    "train_data['CabinLetter'] = train_data['Cabin'].map(lambda x: re.compile(\"([a-zA-Z]+)\").search(x).group())# convert the distinct cabin letters with incremental integer values\n",
    "train_data['CabinLetter'] = pd.factorize(train_data['CabinLetter'])[0]\n",
    "train_data[['CabinLetter','Survived']].groupby(['CabinLetter']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "可见，不同的船舱生存率也有不同，但是差别不大。所以在处理中，我们可以直接将特征删除。\n",
    "\n",
    "(10) 港口和存活与否的关系 Embarked\n",
    "\n",
    "泰坦尼克号从英国的南安普顿港出发，途径法国瑟堡和爱尔兰昆士敦，那么在昆士敦之前上船的人，有可能在瑟堡或昆士敦下船，这些人将不会遇到海难。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f1b3b4e2be0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m1p7oFtHuwGd8eYvo27mMS3bPzDoR3SJaBLQBHnH3y3MbVeNSEhARAczsH4GH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\nuwnrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3afe0748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Embarked', hue='Survived', data=train_data)\n",
    "plt.title('Embarked and Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H+rXhuxEXcHFco4L13ydvo8+YeVaWbsotS1DGBJHP5+Od+SlcP+EXtuzLAKBZ/RpMu+cv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\nGUeNlj5g3HwenGq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KBIOIhAHTgARVbamqbYF/AjYOTgXjvtefAZ+raizO5KzVgedCGphH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\n8SRLUMYYYzzp/wOUa/AgWdPGzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b3b7c7898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Embarked', 'Survived', data=train_data, size=3, aspect=2)\n",
    "plt.title('Embarked and Survived rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "由上可以看出，在不同的港口上船，生还率不同，C最高，Q次之，S最低。\n",
    "\n",
    "以上为所给出的数据特征与生还与否的分析。\n",
    "\n",
    "据了解，泰坦尼克号上共有2224名乘客。本训练数据只给出了891名乘客的信息，\n",
    "如果该数据集是从总共的2224人中随机选出的，根据中心极限定理，该样本的数据也足够大，那么我们的分析结果就具有代表性；\n",
    "但如果不是随机选取，那么我们的分析结果就可能不太靠谱了。\n",
    "\n",
    "(11) 其他可能和存活与否有关系的特征\n",
    "\n",
    "对于数据集中没有给出的特征信息，我们还可以联想其他可能会对模型产生影响的特征因素。\n",
    "如：乘客的国籍、乘客的身高、乘客的体重、乘客是否会游泳、乘客职业等等。\n",
    "\n",
    "另外还有数据集中没有分析的几个特征：Ticket（船票号）、Cabin（船舱号）,这些因素的不同可能会影响乘客在船中的位置从而影响逃生的顺序。\n",
    "但是船舱号数据缺失，船票号类别大，难以分析规律，所以在后期模型融合的时候，将这些因素交由模型来决定其重要性。\n",
    "\n",
    "  4. 变量转换\n",
    "变量转换的目的是将数据转换为适用于模型使用的数据，不同模型接受不同类型的数据，\n",
    "Scikit-learn要求数据都是数字型numeric，所以我们要将一些非数字型的原始数据转换为数字型numeric。\n",
    "\n",
    "所以下面对数据的转换进行介绍，以在进行特征工程的时候使用。\n",
    "\n",
    "所有的数据可以分为两类：\n",
    "\n",
    "1.定量(Quantitative)变量可以以某种方式排序，Age就是一个很好的列子。\n",
    "\n",
    "2.定性(Qualitative)变量描述了物体的某一（不能被数学表示的）方面，Embarked就是一个例子。\n",
    "\n",
    "定性(Qualitative)转换：\n",
    "\n",
    "1. Dummy Variables\n",
    "\n",
    "就是类别变量或者二元变量，当qualitative variable是一些频繁出现的几个独立变量时，\n",
    "Dummy Variables比较适合使用。我们以Embarked为例，Embarked只包含三个值'S','C','Q'，\n",
    "我们可以使用下面的代码将其转换为dummies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "embark_dummies  = pd.get_dummies(train_data['Embarked'])\n",
    "train_data = train_data.join(embark_dummies)\n",
    "train_data.drop(['Embarked'], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>S</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   S  C  Q\n",
       "0  1  0  0\n",
       "1  0  1  0\n",
       "2  1  0  0\n",
       "3  1  0  0\n",
       "4  1  0  0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark_dummies = train_data[['S', 'C', 'Q']]\n",
    "embark_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "2. Factorizing\n",
    "\n",
    "dummy不好处理Cabin（船舱号）这种标称属性，因为他出现的变量比较多。\n",
    "所以Pandas有一个方法叫做factorize()，它可以创建一些数字，来表示类别变量，对每一个类别映射一个ID，这种映射最后只生成一个特征，\n",
    "不像dummy那样生成多个特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Replace missing values with \"U0\"\n",
    "train_data['Cabin'][train_data.Cabin.isnull()] = 'U0'# create feature for the alphabetical part of the cabin number\n",
    "train_data['CabinLetter'] = train_data['Cabin'].map( lambda x : re.compile(\"([a-zA-Z]+)\").search(x).group())# convert the distinct cabin letters with incremental integer values\n",
    "train_data['CabinLetter'] = pd.factorize(train_data['CabinLetter'])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    1\n",
       "4    0\n",
       "Name: CabinLetter, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['CabinLetter'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "定量(Quantitative)转换：\n",
    "\n",
    "1. Scaling\n",
    "\n",
    "Scaling可以将一个很大范围的数值映射到一个很小的范围(通常是-1 - 1，或则是0 - 1)，\n",
    "很多情况下我们需要将数值做Scaling使其范围大小一样，否则大范围数值特征将会由更高的权重。比如：Age的范围可能只是0-100，\n",
    "而income的范围可能是0-10000000，在某些对数组大小敏感的模型中会影响其结果。\n",
    "\n",
    "下面对Age进行Scaling："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "assert np.size(train_data['Age']) == 891# StandardScaler will subtract the mean from each value then scale to the unit variance\n",
    "scaler = preprocessing.StandardScaler()\n",
    "train_data['Age_scaled'] = scaler.fit_transform(train_data['Age'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.558026\n",
       "1    0.607318\n",
       "2   -0.266690\n",
       "3    0.388816\n",
       "4    0.388816\n",
       "Name: Age_scaled, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Age_scaled'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "2. Binning\n",
    "\n",
    "Binning通过观察“邻居”(即周围的值)将连续数据离散化。\n",
    "存储的值被分布到一些“桶”或“箱“”中，就像直方图的bin将数据划分成几块一样。下面的代码对Fare进行Binning。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (-0.001, 7.854]\n",
       "1    (39.688, 512.329]\n",
       "2        (7.854, 10.5]\n",
       "3    (39.688, 512.329]\n",
       "4        (7.854, 10.5]\n",
       "Name: Fare_bin, dtype: category\n",
       "Categories (5, interval[float64]): [(-0.001, 7.854] < (7.854, 10.5] < (10.5, 21.679] < (21.679, 39.688] < (39.688, 512.329]]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Divide all fares into quartiles\n",
    "train_data['Fare_bin'] = pd.qcut(train_data['Fare'], 5)\n",
    "train_data['Fare_bin'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "将数据Bining化后，要么将数据factorize化，要么dummies化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# qcut() creates a new variable that identifies the quartile range, but we can't use the string# so either factorize or create dummies from the result\n",
    "# factorize\n",
    "train_data['Fare_bin_id'] = pd.factorize(train_data['Fare_bin'])[0]\n",
    "# dummies\n",
    "fare_bin_dummies_df = pd.get_dummies(train_data['Fare_bin']).rename(columns=lambda x: 'Fare_' + str(x))\n",
    "train_data = pd.concat([train_data, fare_bin_dummies_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " 5. 特征工程\n",
    "在进行特征工程的时候，我们不仅需要对训练数据进行处理，还需要同时将测试数据同训练数据一起处理，使得二者具有相同的数据类型和数据分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df_org = pd.read_csv('./input/train.csv')\n",
    "test_df_org = pd.read_csv('./input/test.csv')\n",
    "test_df_org['Survived'] = 0\n",
    "combined_train_test = train_df_org.append(test_df_org)\n",
    "PassengerId = test_df_org['PassengerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "对数据进行特征工程，也就是从各项参数中提取出对输出结果有或大或小的影响的特征，将这些特征作为训练模型的依据。\n",
    "\n",
    "一般来说，我们会先从含有缺失值的特征开始。\n",
    "\n",
    "(1) Embarked\n",
    "\n",
    "因为“Embarked”项的缺失值不多，所以这里我们以众数来填充："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_train_test['Embarked'].fillna(combined_train_test['Embarked'].mode().iloc[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "对于三种不同的港口，由上面介绍的数值转换，我们知道可以有两种特征处理方式：dummy和facrorizing。因为只有三个港口，所以我们可以直接用dummy来处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 为了后面的特征分析，这里我们将 Embarked 特征进行facrorizing\n",
    "combined_train_test['Embarked'] = pd.factorize(combined_train_test['Embarked'])[0]# 使用 pd.get_dummies 获取one-hot 编码\n",
    "emb_dummies_df = pd.get_dummies(combined_train_test['Embarked'], prefix=combined_train_test[['Embarked']].columns[0])\n",
    "combined_train_test = pd.concat([combined_train_test, emb_dummies_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(2) Sex\n",
    "\n",
    "对Sex也进行one-hot编码，也就是dummy处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 为了后面的特征分析，这里我们也将 Sex 特征进行facrorizing\n",
    "combined_train_test['Sex'] = pd.factorize(combined_train_test['Sex'])[0]\n",
    "sex_dummies_df = pd.get_dummies(combined_train_test['Sex'], prefix=combined_train_test[['Sex']].columns[0])\n",
    "combined_train_test = pd.concat([combined_train_test, sex_dummies_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(3) Name\n",
    "\n",
    "首先先从名字中提取各种称呼："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# what is each person's title? \n",
    "combined_train_test['Title'] = combined_train_test['Name'].map(lambda x: re.compile(\", (.*?)\\.\").findall(x)[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "将各式称呼进行统一化处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "title_Dict = {}\n",
    "title_Dict.update(dict.fromkeys(['Capt', 'Col', 'Major', 'Dr', 'Rev'], 'Officer'))\n",
    "title_Dict.update(dict.fromkeys(['Don', 'Sir', 'the Countess', 'Dona', 'Lady'], 'Royalty'))\n",
    "title_Dict.update(dict.fromkeys(['Mme', 'Ms', 'Mrs'], 'Mrs'))\n",
    "title_Dict.update(dict.fromkeys(['Mlle', 'Miss'], 'Miss'))\n",
    "title_Dict.update(dict.fromkeys(['Mr'], 'Mr'))\n",
    "title_Dict.update(dict.fromkeys(['Master','Jonkheer'], 'Master'))\n",
    "combined_train_test['Title'] = combined_train_test['Title'].map(title_Dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "使用dummy对不同的称呼进行分列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 为了后面的特征分析，这里我们也将 Title 特征进行facrorizing\n",
    "combined_train_test['Title'] = pd.factorize(combined_train_test['Title'])[0]\n",
    "title_dummies_df = pd.get_dummies(combined_train_test['Title'], prefix=combined_train_test[['Title']].columns[0])\n",
    "combined_train_test = pd.concat([combined_train_test, title_dummies_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "增加名字长度的特征："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_train_test['Name_length'] = combined_train_test['Name'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(4) Fare\n",
    "\n",
    "由前面分析可以知道，Fare项在测试数据中缺少一个值，所以需要对该值进行填充。\n",
    "\n",
    "我们按照一二三等舱各自的均价来填充：\n",
    "\n",
    "下面transform将函数np.mean应用到各个group中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_train_test['Fare'] = combined_train_test[['Fare']].fillna(combined_train_test.groupby('Pclass').transform(np.mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "通过对Ticket数据的分析，我们可以看到部分票号数据有重复，同时结合亲属人数及名字的数据，和票价船舱等级对比，\n",
    "我们可以知道购买的票中有家庭票和团体票，所以我们需要将团体票的票价分配到每个人的头上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_train_test['Group_Ticket'] = combined_train_test['Fare'].groupby(by=combined_train_test['Ticket']).transform('count')\n",
    "combined_train_test['Fare'] = combined_train_test['Fare'] / combined_train_test['Group_Ticket']\n",
    "combined_train_test.drop(['Group_Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "使用binning给票价分等级："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_train_test['Fare_bin'] = pd.qcut(combined_train_test['Fare'], 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "对于5个等级的票价我们也可以继续使用dummy为票价等级分列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_train_test['Fare_bin_id'] = pd.factorize(combined_train_test['Fare_bin'])[0]\n",
    "fare_bin_dummies_df = pd.get_dummies(combined_train_test['Fare_bin_id']).rename(columns=lambda x: 'Fare_' + str(x))\n",
    "combined_train_test = pd.concat([combined_train_test, fare_bin_dummies_df], axis=1)\n",
    "combined_train_test.drop(['Fare_bin'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(5) Pclass\n",
    "\n",
    "Pclass这一项，其实已经可以不用继续处理了，我们只需要将其转换为dummy形式即可。\n",
    "\n",
    "但是为了更好的分析问题，我们这里假设对于不同等级的船舱，各船舱内部的票价也说明了各等级舱的位置，\n",
    "那么也就很有可能与逃生的顺序有关系。所以这里分出每等舱里的高价和低价位。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "# 建立PClass Fare Category\n",
    "def pclass_fare_category(df, pclass1_mean_fare, pclass2_mean_fare, pclass3_mean_fare):\n",
    "    if df['Pclass'] == 1:\n",
    "        if df['Fare'] <= pclass1_mean_fare:\n",
    "            return 'Pclass1_Low'\n",
    "        else:\n",
    "            return 'Pclass1_High'\n",
    "    elif df['Pclass'] == 2:\n",
    "        if df['Fare'] <= pclass2_mean_fare:\n",
    "            return 'Pclass2_Low'\n",
    "        else:\n",
    "            return 'Pclass2_High'\n",
    "    elif df['Pclass'] == 3:\n",
    "        if df['Fare'] <= pclass3_mean_fare:\n",
    "            return 'Pclass3_Low'\n",
    "        else:\n",
    "            return 'Pclass3_High'\n",
    "Pclass1_mean_fare = combined_train_test['Fare'].groupby(by=combined_train_test['Pclass']).mean().get([1]).values[0]\n",
    "Pclass2_mean_fare = combined_train_test['Fare'].groupby(by=combined_train_test['Pclass']).mean().get([2]).values[0]\n",
    "Pclass3_mean_fare = combined_train_test['Fare'].groupby(by=combined_train_test['Pclass']).mean().get([3]).values[0]\n",
    "        # 建立Pclass_Fare Category\n",
    "combined_train_test['Pclass_Fare_Category'] = combined_train_test.apply(pclass_fare_category, args=(\n",
    " Pclass1_mean_fare, Pclass2_mean_fare, Pclass3_mean_fare), axis=1)\n",
    "pclass_level = LabelEncoder()# 给每一项添加标签\n",
    "pclass_level.fit(np.array(\n",
    " ['Pclass1_Low', 'Pclass1_High', 'Pclass2_Low', 'Pclass2_High', 'Pclass3_Low', 'Pclass3_High']))\n",
    "# 转换成数值\n",
    "combined_train_test['Pclass_Fare_Category'] = pclass_level.transform(combined_train_test['Pclass_Fare_Category'])\n",
    "# dummy 转换\n",
    "pclass_dummies_df = pd.get_dummies(combined_train_test['Pclass_Fare_Category']).rename(columns=lambda x: 'Pclass_' + str(x))\n",
    "combined_train_test = pd.concat([combined_train_test, pclass_dummies_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "同时，我们将 Pclass 特征factorize化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_train_test['Pclass'] = pd.factorize(combined_train_test['Pclass'])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(6) Parch and SibSp\n",
    "\n",
    "由前面的分析，我们可以知道，亲友的数量没有或者太多会影响到Survived。所以将二者合并为FamliySize这一组合项，同时也保留这两项。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def family_size_category(family_size):\n",
    "    if family_size <= 1:\n",
    "        return 'Single'\n",
    "    elif family_size <= 4:\n",
    "        return 'Small_Family'\n",
    "    else:\n",
    "        return 'Large_Family'\n",
    "combined_train_test['Family_Size'] = combined_train_test['Parch'] + combined_train_test['SibSp'] + 1\n",
    "combined_train_test['Family_Size_Category'] = combined_train_test['Family_Size'].map(family_size_category)\n",
    "le_family = LabelEncoder()\n",
    "le_family.fit(np.array(['Single', 'Small_Family', 'Large_Family']))\n",
    "combined_train_test['Family_Size_Category'] = le_family.transform(combined_train_test['Family_Size_Category'])\n",
    "family_size_dummies_df = pd.get_dummies(combined_train_test['Family_Size_Category'],\n",
    "                                     prefix=combined_train_test[['Family_Size_Category']].columns[0])\n",
    "combined_train_test = pd.concat([combined_train_test, family_size_dummies_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(7) Age\n",
    "\n",
    "因为Age项的缺失值较多，所以不能直接填充age的众数或者平均数。\n",
    "\n",
    "常见的有两种对年龄的填充方式：一种是根据Title中的称呼，如Mr，Master、Miss等称呼不同类别的人的平均年龄来填充；一种是综合几项如Sex、Title、Pclass等其他没有缺失值的项，使用机器学习算法来预测Age。\n",
    "\n",
    "这里我们使用后者来处理。以Age为目标值，将Age完整的项作为训练集，将Age缺失的项作为测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "missing_age_df = pd.DataFrame(combined_train_test[\n",
    " ['Age', 'Embarked', 'Sex', 'Title', 'Name_length', 'Family_Size', 'Family_Size_Category','Fare', 'Fare_bin_id', 'Pclass']])\n",
    "missing_age_train = missing_age_df[missing_age_df['Age'].notnull()]\n",
    "missing_age_test = missing_age_df[missing_age_df['Age'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Title</th>\n",
       "      <th>Name_length</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Family_Size_Category</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Fare_bin_id</th>\n",
       "      <th>Pclass</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>16</td>\n",
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       "      <td>8.4583</td>\n",
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       "      <td>0</td>\n",
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       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Embarked  Sex  Title  Name_length  Family_Size  Family_Size_Category  \\\n",
       "5   NaN         2    0      0           16            1                     1   \n",
       "17  NaN         0    0      0           28            1                     1   \n",
       "19  NaN         1    1      1           23            1                     1   \n",
       "26  NaN         1    0      0           23            1                     1   \n",
       "28  NaN         2    1      2           29            1                     1   \n",
       "\n",
       "       Fare  Fare_bin_id  Pclass  \n",
       "5    8.4583            2       0  \n",
       "17  13.0000            3       2  \n",
       "19   7.2250            4       0  \n",
       "26   7.2250            4       0  \n",
       "28   7.8792            0       0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_age_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "建立Age的预测模型，我们可以多模型预测，然后再做模型的融合，提高预测的精度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import ensemble\n",
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "def fill_missing_age(missing_age_train, missing_age_test):\n",
    "    missing_age_X_train = missing_age_train.drop(['Age'], axis=1)\n",
    "    missing_age_Y_train = missing_age_train['Age']\n",
    "    missing_age_X_test = missing_age_test.drop(['Age'], axis=1)\n",
    "\n",
    "    # model 1  gbm\n",
    "    gbm_reg = GradientBoostingRegressor(random_state=42)\n",
    "    gbm_reg_param_grid = {'n_estimators': [2000], 'max_depth': [4], 'learning_rate': [0.01], 'max_features': [3]}\n",
    "    gbm_reg_grid = model_selection.GridSearchCV(gbm_reg, gbm_reg_param_grid, cv=10, n_jobs=25, verbose=1, scoring='neg_mean_squared_error')\n",
    "    gbm_reg_grid.fit(missing_age_X_train, missing_age_Y_train)\n",
    "    print('Age feature Best GB Params:' + str(gbm_reg_grid.best_params_))\n",
    "    print('Age feature Best GB Score:' + str(gbm_reg_grid.best_score_))\n",
    "    print('GB Train Error for \"Age\" Feature Regressor:' + str(gbm_reg_grid.score(missing_age_X_train, missing_age_Y_train)))\n",
    "    missing_age_test.loc[:, 'Age_GB'] = gbm_reg_grid.predict(missing_age_X_test)\n",
    "    print(missing_age_test['Age_GB'][:4])\n",
    "    \n",
    "    # model 2 rf\n",
    "    rf_reg = RandomForestRegressor()\n",
    "    rf_reg_param_grid = {'n_estimators': [200], 'max_depth': [5], 'random_state': [0]}\n",
    "    rf_reg_grid = model_selection.GridSearchCV(rf_reg, rf_reg_param_grid, cv=10, n_jobs=25, verbose=1, scoring='neg_mean_squared_error')\n",
    "    rf_reg_grid.fit(missing_age_X_train, missing_age_Y_train)\n",
    "    print('Age feature Best RF Params:' + str(rf_reg_grid.best_params_))\n",
    "    print('Age feature Best RF Score:' + str(rf_reg_grid.best_score_))\n",
    "    print('RF Train Error for \"Age\" Feature Regressor' + str(rf_reg_grid.score(missing_age_X_train, missing_age_Y_train)))\n",
    "    missing_age_test.loc[:, 'Age_RF'] = rf_reg_grid.predict(missing_age_X_test)\n",
    "    print(missing_age_test['Age_RF'][:4])\n",
    "\n",
    "    # two models merge\n",
    "    print('shape1', missing_age_test['Age'].shape, missing_age_test[['Age_GB', 'Age_RF']].mode(axis=1).shape)\n",
    "    # missing_age_test['Age'] = missing_age_test[['Age_GB', 'Age_LR']].mode(axis=1)\n",
    "\n",
    "    missing_age_test.loc[:, 'Age'] = np.mean([missing_age_test['Age_GB'], missing_age_test['Age_RF']])\n",
    "    print(missing_age_test['Age'][:4])\n",
    "\n",
    "    missing_age_test.drop(['Age_GB', 'Age_RF'], axis=1, inplace=True)\n",
    "\n",
    "    return missing_age_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "利用融合模型预测的结果填充Age的缺失值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=25)]: Done   5 out of  10 | elapsed:    1.3s remaining:    1.3s\n",
      "[Parallel(n_jobs=25)]: Done  10 out of  10 | elapsed:    1.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age feature Best GB Params:{'learning_rate': 0.01, 'max_depth': 4, 'max_features': 3, 'n_estimators': 2000}\n",
      "Age feature Best GB Score:-130.295677599\n",
      "GB Train Error for \"Age\" Feature Regressor:-64.6566961723\n",
      "5     35.773942\n",
      "17    31.489153\n",
      "19    34.113840\n",
      "26    28.621281\n",
      "Name: Age_GB, dtype: float64\n",
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=25)]: Done   5 out of  10 | elapsed:    0.6s remaining:    0.6s\n",
      "[Parallel(n_jobs=25)]: Done  10 out of  10 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age feature Best RF Params:{'max_depth': 5, 'n_estimators': 200, 'random_state': 0}\n",
      "Age feature Best RF Score:-119.094956052\n",
      "RF Train Error for \"Age\" Feature Regressor-96.0603148448\n",
      "5     33.459421\n",
      "17    33.076798\n",
      "19    34.855942\n",
      "26    28.146718\n",
      "Name: Age_RF, dtype: float64\n",
      "shape1 (263,) (263, 2)\n",
      "5     30.000675\n",
      "17    30.000675\n",
      "19    30.000675\n",
      "26    30.000675\n",
      "Name: Age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "combined_train_test.loc[(combined_train_test.Age.isnull()), 'Age'] = fill_missing_age(missing_age_train, missing_age_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(8) Ticket\n",
    "\n",
    "观察Ticket的值，我们可以看到，Ticket有字母和数字之分，而对于不同的字母，可能在很大程度上就意味着船舱等级或者不同船舱的位置，\n",
    "也会对Survived产生一定的影响，所以我们将Ticket中的字母分开，为数字的部分则分为一类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_train_test['Ticket_Letter'] = combined_train_test['Ticket'].str.split().str[0]\n",
    "combined_train_test['Ticket_Letter'] = combined_train_test['Ticket_Letter'].apply(lambda x: 'U0' if x.isnumeric() else x)\n",
    "# 如果要提取数字信息，则也可以这样做，现在我们对数字票单纯地分为一类。# \n",
    "combined_train_test['Ticket_Number'] = combined_train_test['Ticket'].apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
    "# \n",
    "combined_train_test['Ticket_Number'].fillna(0, inplace=True)\n",
    "#将Ticket_Letter factorize\n",
    "combined_train_test['Ticket_Letter'] = pd.factorize(combined_train_test['Ticket_Letter'])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(9) Cabin\n",
    "\n",
    "因为Cabin项的缺失值确实太多了，我们很难对其进行分析，或者预测。\n",
    "所以这里我们可以直接将Cabin这一项特征去除。\n",
    "但通过上面的分析，可以知道，该特征信息的有无也与生存率有一定的关系，所以这里我们暂时保留该特征，并将其分为有和无两类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_train_test.loc[combined_train_test.Cabin.isnull(), 'Cabin'] = 'U0'\n",
    "combined_train_test['Cabin'] = combined_train_test['Cabin'].apply(lambda x: 0 if x == 'U0' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "特征间相关性分析\n",
    "\n",
    "我们挑选一些主要的特征，生成特征之间的关联图，查看特征与特征之间的相关性："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Correlation = pd.DataFrame(combined_train_test[\n",
    " ['Embarked', 'Sex', 'Title', 'Name_length', 'Family_Size', 'Family_Size_Category','Fare', 'Fare_bin_id', 'Pclass', \n",
    "  'Pclass_Fare_Category', 'Age', 'Ticket_Letter', 'Cabin']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d9fe0c860>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x6Wm0cHD6x9vxtXfEWK3hekHnqqo41jInJr1Efsd/nr+q2VuaE5tSlDsuJR0H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\nCE1Lq3eZMSgVCpb0H8aMfVtRFarZPWkGh+NjiM6tKf8UdT4vBx/gSb/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PAAAg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m0qRJhu+Uubm5sWJF4SlNBTurAFeuXMHLywsPj8fvt4aEEEIIIYR4mpXdDxWJR/a///2P48eP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\niikAAAAA6IP/AeEYe0vXrVpTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d9fe0c2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap = plt.cm.viridis\n",
    "plt.figure(figsize=(14,12))\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "sns.heatmap(Correlation.astype(float).corr(),linewidths=0.1,vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "特征之间的数据分布图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f9d7f996400>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1K9HcXAYAuK5exaRTHWnHjnjjShxO7GeZ+6CnUS5bX38a54w7O738hgYozz6b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\n8PhlGe2HD6fKKSuDkliXUb7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6BYnjQSyWnpeyMtm+3nADfBW+xKHJB/EZ03GmogL+ijLL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pf5lEtRrLPUHLfjmNSSPgcsFAKT1RugQDKFbKg7OihxGSvTxM2m9asQzp5N+eBky1130fXRKK3Y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AkNevxwmsQyymYGREON8D3nXI94FlAxTkphHpAA2xGNjx4/l67O8XukoHhCgWvGGqZyt8TIKc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\ne91u4MCBRDhk2TL2PS4///xGiA4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9QqrKPBSJIgBFBhEIVIgghF3Xpjo4HCm7W+d7a3Ud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\n8LY0+k+HagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d9c4bab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(combined_train_test[[u'Survived', u'Pclass', u'Sex', u'Age', u'Fare', u'Embarked',\n",
    "    u'Family_Size', u'Title', u'Ticket_Letter']], hue='Survived', palette = 'seismic',size=1.2,diag_kind = 'kde',diag_kws=dict(shade=True),plot_kws=dict(s=10) )\n",
    "g.set(xticklabels=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "输入模型前的一些处理：\n",
    "\n",
    "1. 一些数据的正则化\n",
    "\n",
    "这里我们将Age和fare进行正则化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "scale_age_fare = preprocessing.StandardScaler().fit(combined_train_test[['Age','Fare', 'Name_length']])\n",
    "combined_train_test[['Age','Fare', 'Name_length']] = scale_age_fare.transform(combined_train_test[['Age','Fare', 'Name_length']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "2. 弃掉无用特征\n",
    "\n",
    "对于上面的特征工程中，我们从一些原始的特征中提取出了很多要融合到模型中的特征，但是我们需要剔除那些原本的我们用不到的或者非数值特征。\n",
    "\n",
    "首先对我们的数据先进行一下备份，以便后期的再次分析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data_backup = combined_train_test\n",
    "combined_train_test.drop(['PassengerId', 'Embarked', 'Sex', 'Name', 'Title', 'Fare_bin_id', 'Pclass_Fare_Category', \n",
    "                       'Parch', 'SibSp', 'Family_Size_Category', 'Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "3. 将训练数据和测试数据分开："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = combined_train_test[:891]\n",
    "test_data = combined_train_test[891:]\n",
    "titanic_train_data_X = train_data.drop(['Survived'],axis=1)\n",
    "titanic_train_data_Y = train_data['Survived']\n",
    "titanic_test_data_X = test_data.drop(['Survived'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 33)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train_data_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "  6. 模型融合及测试\n",
    "模型融合的过程需要分几步来进行。\n",
    "\n",
    "(1) 利用不同的模型来对特征进行筛选，选出较为重要的特征："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "def get_top_n_features(titanic_train_data_X, titanic_train_data_Y, top_n_features):\n",
    "\n",
    "    # random forest\n",
    "    rf_est = RandomForestClassifier(random_state=0)\n",
    "    rf_param_grid = {'n_estimators': [500], 'min_samples_split': [2, 3], 'max_depth': [20]}\n",
    "    rf_grid = model_selection.GridSearchCV(rf_est, rf_param_grid, n_jobs=25, cv=10, verbose=1)\n",
    "    rf_grid.fit(titanic_train_data_X, titanic_train_data_Y)\n",
    "    print('Top N Features Best RF Params:' + str(rf_grid.best_params_))\n",
    "    print('Top N Features Best RF Score:' + str(rf_grid.best_score_))\n",
    "    print('Top N Features RF Train Score:' + str(rf_grid.score(titanic_train_data_X, titanic_train_data_Y)))\n",
    "    feature_imp_sorted_rf = pd.DataFrame({'feature': list(titanic_train_data_X),\n",
    "                                          'importance': rf_grid.best_estimator_.feature_importances_}).sort_values('importance', ascending=False)\n",
    "    features_top_n_rf = feature_imp_sorted_rf.head(top_n_features)['feature']\n",
    "    print('Sample 10 Features from RF Classifier')\n",
    "    print(str(features_top_n_rf[:10]))\n",
    "\n",
    "    # AdaBoost\n",
    "    ada_est =AdaBoostClassifier(random_state=0)\n",
    "    ada_param_grid = {'n_estimators': [500], 'learning_rate': [0.01, 0.1]}\n",
    "    ada_grid = model_selection.GridSearchCV(ada_est, ada_param_grid, n_jobs=25, cv=10, verbose=1)\n",
    "    ada_grid.fit(titanic_train_data_X, titanic_train_data_Y)\n",
    "    print('Top N Features Best Ada Params:' + str(ada_grid.best_params_))\n",
    "    print('Top N Features Best Ada Score:' + str(ada_grid.best_score_))\n",
    "    print('Top N Features Ada Train Score:' + str(ada_grid.score(titanic_train_data_X, titanic_train_data_Y)))\n",
    "    feature_imp_sorted_ada = pd.DataFrame({'feature': list(titanic_train_data_X),\n",
    "                                           'importance': ada_grid.best_estimator_.feature_importances_}).sort_values('importance', ascending=False)\n",
    "    features_top_n_ada = feature_imp_sorted_ada.head(top_n_features)['feature']\n",
    "    print('Sample 10 Feature from Ada Classifier:')\n",
    "    print(str(features_top_n_ada[:10]))\n",
    "\n",
    "    # ExtraTree\n",
    "    et_est = ExtraTreesClassifier(random_state=0)\n",
    "    et_param_grid = {'n_estimators': [500], 'min_samples_split': [3, 4], 'max_depth': [20]}\n",
    "    et_grid = model_selection.GridSearchCV(et_est, et_param_grid, n_jobs=25, cv=10, verbose=1)\n",
    "    et_grid.fit(titanic_train_data_X, titanic_train_data_Y)\n",
    "    print('Top N Features Best ET Params:' + str(et_grid.best_params_))\n",
    "    print('Top N Features Best ET Score:' + str(et_grid.best_score_))\n",
    "    print('Top N Features ET Train Score:' + str(et_grid.score(titanic_train_data_X, titanic_train_data_Y)))\n",
    "    feature_imp_sorted_et = pd.DataFrame({'feature': list(titanic_train_data_X),\n",
    "                                          'importance': et_grid.best_estimator_.feature_importances_}).sort_values('importance', ascending=False)\n",
    "    features_top_n_et = feature_imp_sorted_et.head(top_n_features)['feature']\n",
    "    print('Sample 10 Features from ET Classifier:')\n",
    "    print(str(features_top_n_et[:10]))\n",
    "    \n",
    "    # GradientBoosting\n",
    "    gb_est =GradientBoostingClassifier(random_state=0)\n",
    "    gb_param_grid = {'n_estimators': [500], 'learning_rate': [0.01, 0.1], 'max_depth': [20]}\n",
    "    gb_grid = model_selection.GridSearchCV(gb_est, gb_param_grid, n_jobs=25, cv=10, verbose=1)\n",
    "    gb_grid.fit(titanic_train_data_X, titanic_train_data_Y)\n",
    "    print('Top N Features Best GB Params:' + str(gb_grid.best_params_))\n",
    "    print('Top N Features Best GB Score:' + str(gb_grid.best_score_))\n",
    "    print('Top N Features GB Train Score:' + str(gb_grid.score(titanic_train_data_X, titanic_train_data_Y)))\n",
    "    feature_imp_sorted_gb = pd.DataFrame({'feature': list(titanic_train_data_X),\n",
    "                                           'importance': gb_grid.best_estimator_.feature_importances_}).sort_values('importance', ascending=False)\n",
    "    features_top_n_gb = feature_imp_sorted_gb.head(top_n_features)['feature']\n",
    "    print('Sample 10 Feature from GB Classifier:')\n",
    "    print(str(features_top_n_gb[:10]))\n",
    "    \n",
    "    # DecisionTree\n",
    "    dt_est = DecisionTreeClassifier(random_state=0)\n",
    "    dt_param_grid = {'min_samples_split': [2, 4], 'max_depth': [20]}\n",
    "    dt_grid = model_selection.GridSearchCV(dt_est, dt_param_grid, n_jobs=25, cv=10, verbose=1)\n",
    "    dt_grid.fit(titanic_train_data_X, titanic_train_data_Y)\n",
    "    print('Top N Features Best DT Params:' + str(dt_grid.best_params_))\n",
    "    print('Top N Features Best DT Score:' + str(dt_grid.best_score_))\n",
    "    print('Top N Features DT Train Score:' + str(dt_grid.score(titanic_train_data_X, titanic_train_data_Y)))\n",
    "    feature_imp_sorted_dt = pd.DataFrame({'feature': list(titanic_train_data_X),\n",
    "                                          'importance': dt_grid.best_estimator_.feature_importances_}).sort_values('importance', ascending=False)\n",
    "    features_top_n_dt = feature_imp_sorted_dt.head(top_n_features)['feature']\n",
    "    print('Sample 10 Features from DT Classifier:')\n",
    "    print(str(features_top_n_dt[:10]))\n",
    "    \n",
    "    # merge the three models\n",
    "    features_top_n = pd.concat([features_top_n_rf, features_top_n_ada, features_top_n_et, features_top_n_gb, features_top_n_dt], \n",
    "                               ignore_index=True).drop_duplicates()\n",
    "    \n",
    "    features_importance = pd.concat([feature_imp_sorted_rf, feature_imp_sorted_ada, feature_imp_sorted_et, \n",
    "                                   feature_imp_sorted_gb, feature_imp_sorted_dt],ignore_index=True)\n",
    "    \n",
    "    return features_top_n , features_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(2) 依据我们筛选出的特征构建训练集和测试集\n",
    "\n",
    "但如果在进行特征工程的过程中，产生了大量的特征，而特征与特征之间会存在一定的相关性。\n",
    "太多的特征一方面会影响模型训练的速度，另一方面也可能会使得模型过拟合。\n",
    "所以在特征太多的情况下，我们可以利用不同的模型对特征进行筛选，选取出我们想要的前n个特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=25)]: Done  13 out of  20 | elapsed:    1.0s remaining:    0.5s\n",
      "[Parallel(n_jobs=25)]: Done  20 out of  20 | elapsed:    1.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top N Features Best RF Params:{'max_depth': 20, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "Top N Features Best RF Score:0.841750841751\n",
      "Top N Features RF Train Score:0.987654320988\n",
      "Sample 10 Features from RF Classifier\n",
      "15      Name_length\n",
      "0               Age\n",
      "2              Fare\n",
      "9           Title_0\n",
      "32    Ticket_Number\n",
      "7             Sex_0\n",
      "8             Sex_1\n",
      "3            Pclass\n",
      "27      Family_Size\n",
      "31    Ticket_Letter\n",
      "Name: feature, dtype: object\n",
      "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=25)]: Done  13 out of  20 | elapsed:    1.1s remaining:    0.6s\n",
      "[Parallel(n_jobs=25)]: Done  20 out of  20 | elapsed:    1.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top N Features Best Ada Params:{'learning_rate': 0.1, 'n_estimators': 500}\n",
      "Top N Features Best Ada Score:0.82379349046\n",
      "Top N Features Ada Train Score:0.846240179574\n",
      "Sample 10 Feature from Ada Classifier:\n",
      "0               Age\n",
      "32    Ticket_Number\n",
      "2              Fare\n",
      "15      Name_length\n",
      "31    Ticket_Letter\n",
      "12          Title_3\n",
      "9           Title_0\n",
      "27      Family_Size\n",
      "10          Title_1\n",
      "14          Title_5\n",
      "Name: feature, dtype: object\n",
      "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=25)]: Done  13 out of  20 | elapsed:    1.0s remaining:    0.5s\n",
      "[Parallel(n_jobs=25)]: Done  20 out of  20 | elapsed:    1.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top N Features Best ET Params:{'max_depth': 20, 'min_samples_split': 4, 'n_estimators': 500}\n",
      "Top N Features Best ET Score:0.83950617284\n",
      "Top N Features ET Train Score:0.971941638608\n",
      "Sample 10 Features from ET Classifier:\n",
      "9                    Title_0\n",
      "7                      Sex_0\n",
      "8                      Sex_1\n",
      "15               Name_length\n",
      "0                        Age\n",
      "32             Ticket_Number\n",
      "2                       Fare\n",
      "1                      Cabin\n",
      "11                   Title_2\n",
      "30    Family_Size_Category_2\n",
      "Name: feature, dtype: object\n",
      "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=25)]: Done  13 out of  20 | elapsed:    3.8s remaining:    2.1s\n",
      "[Parallel(n_jobs=25)]: Done  20 out of  20 | elapsed:    4.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top N Features Best GB Params:{'learning_rate': 0.1, 'max_depth': 20, 'n_estimators': 500}\n",
      "Top N Features Best GB Score:0.775533108866\n",
      "Top N Features GB Train Score:0.998877665544\n",
      "Sample 10 Feature from GB Classifier:\n",
      "32    Ticket_Number\n",
      "2              Fare\n",
      "0               Age\n",
      "15      Name_length\n",
      "9           Title_0\n",
      "31    Ticket_Letter\n",
      "14          Title_5\n",
      "11          Title_2\n",
      "3            Pclass\n",
      "27      Family_Size\n",
      "Name: feature, dtype: object\n",
      "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n",
      "Top N Features Best DT Params:{'max_depth': 20, 'min_samples_split': 4}\n",
      "Top N Features Best DT Score:0.776655443322\n",
      "Top N Features DT Train Score:0.970819304153\n",
      "Sample 10 Features from DT Classifier:\n",
      "9           Title_0\n",
      "32    Ticket_Number\n",
      "2              Fare\n",
      "0               Age\n",
      "27      Family_Size\n",
      "15      Name_length\n",
      "14          Title_5\n",
      "26         Pclass_5\n",
      "31    Ticket_Letter\n",
      "3            Pclass\n",
      "Name: feature, dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=25)]: Done  13 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=25)]: Done  20 out of  20 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "feature_to_pick = 30\n",
    "feature_top_n, feature_importance = get_top_n_features(titanic_train_data_X, titanic_train_data_Y, feature_to_pick)\n",
    "titanic_train_data_X = pd.DataFrame(titanic_train_data_X[feature_top_n])\n",
    "titanic_test_data_X = pd.DataFrame(titanic_test_data_X[feature_top_n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "用视图可视化不同算法筛选的特征排序："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiTAkESSJEmSJAkwJJEkSZIkSQIMSSRJkiRJ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\nJEmSJEmSAEMSSZIkSZIkAP4fadyi4rOdsHYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d5f58c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_feature_imp = feature_importance[:10]\n",
    "Ada_feature_imp = feature_importance[32:32+10].reset_index(drop=True)\n",
    "# make importances relative to max importance\n",
    "rf_feature_importance = 100.0 * (rf_feature_imp['importance'] / rf_feature_imp['importance'].max())\n",
    "Ada_feature_importance = 100.0 * (Ada_feature_imp['importance'] / Ada_feature_imp['importance'].max())\n",
    "# Get the indexes of all features over the importance threshold\n",
    "rf_important_idx = np.where(rf_feature_importance)[0]\n",
    "Ada_important_idx = np.where(Ada_feature_importance)[0]\n",
    "# Adapted from Gradient Boosting regression\n",
    "pos = np.arange(rf_important_idx.shape[0]) + .5\n",
    "plt.figure(1, figsize = (18, 8))\n",
    "plt.subplot(121)\n",
    "plt.barh(pos, rf_feature_importance[rf_important_idx][::-1])\n",
    "plt.yticks(pos, rf_feature_imp['feature'][::-1])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('RandomForest Feature Importance')\n",
    "plt.subplot(122)\n",
    "plt.barh(pos, Ada_feature_importance[Ada_important_idx][::-1])\n",
    "plt.yticks(pos, Ada_feature_imp['feature'][::-1])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('AdaBoost Feature Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(3) 模型融合（Model Ensemble）\n",
    "\n",
    "常见的模型融合方法有：Bagging、Boosting、Stacking、Blending。\n",
    "\n",
    "(3-1):Bagging\n",
    "\n",
    "Bagging 将多个模型，也就是多个基学习器的预测结果进行简单的加权平均或者投票。它的好处是可以并行地训练基学习器。Random Forest就用到了Bagging的思想。\n",
    "\n",
    "(3-2): Boosting\n",
    "\n",
    "Boosting 的思想有点像知错能改，每个基学习器是在上一个基学习器学习的基础上，对上一个基学习器的错误进行弥补。我们将会用到的 AdaBoost，Gradient Boost 就用到了这种思想。\n",
    "\n",
    "(3-3): Stacking\n",
    "\n",
    "Stacking是用新的次学习器去学习如何组合上一层的基学习器。如果把 Bagging 看作是多个基分类器的线性组合，那么Stacking就是多个基分类器的非线性组合。Stacking可以将学习器一层一层地堆砌起来，形成一个网状的结构。\n",
    "\n",
    "相比来说Stacking的融合框架相对前面的二者来说在精度上确实有一定的提升，所以在下面的模型融合上，我们也使用Stacking方法。\n",
    "\n",
    "(3-4): Blending\n",
    "\n",
    "Blending 和 Stacking 很相似，但同时它可以防止信息泄露的问题。\n",
    "\n",
    "Stacking框架融合:\n",
    "\n",
    "这里我们使用了两层的模型融合，Level 1使用了：RandomForest、AdaBoost、ExtraTrees、GBDT、DecisionTree、KNN、SVM ，一共7个模型，Level 2使用了XGBoost使用第一层预测的结果作为特征对最终的结果进行预测。\n",
    "\n",
    "Level 1：\n",
    "\n",
    "Stacking框架是堆叠使用基础分类器的预测作为对二级模型的训练的输入。 然而，我们不能简单地在全部训练数据上训练基本模型，产生预测，输出用于第二层的训练。如果我们在Train Data上训练，然后在Train Data上预测，就会造成标签。为了避免标签，我们需要对每个基学习器使用K-fold，将K个模型对Valid Set的预测结果拼起来，作为下一层学习器的输入。\n",
    "\n",
    "所以这里我们建立输出K-fold预测的方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "# Some useful parameters which will come in handy later on\n",
    "ntrain = titanic_train_data_X.shape[0]\n",
    "ntest = titanic_test_data_X.shape[0]\n",
    "SEED = 0 # for reproducibility\n",
    "NFOLDS = 7 # set folds for out-of-fold prediction\n",
    "kf = KFold(n_splits = NFOLDS, random_state=SEED, shuffle=False)\n",
    "def get_out_fold(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.split(x_train)):\n",
    "        x_tr = x_train[train_index]\n",
    "        y_tr = y_train[train_index]\n",
    "        x_te = x_train[test_index]\n",
    "\n",
    "        clf.fit(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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "构建不同的基学习器，这里我们使用了RandomForest、AdaBoost、ExtraTrees、GBDT、DecisionTree、KNN、SVM 七个基学习器：\n",
    "（这里的模型可以使用如上面的GridSearch方法对模型的超参数进行搜索选择）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "rf = RandomForestClassifier(n_estimators=500, warm_start=True, max_features='sqrt',max_depth=6, \n",
    "                            min_samples_split=3, min_samples_leaf=2, n_jobs=-1, verbose=0)\n",
    "ada = AdaBoostClassifier(n_estimators=500, learning_rate=0.1)\n",
    "et = ExtraTreesClassifier(n_estimators=500, n_jobs=-1, max_depth=8, min_samples_leaf=2, verbose=0)\n",
    "gb = GradientBoostingClassifier(n_estimators=500, learning_rate=0.008, min_samples_split=3, min_samples_leaf=2, max_depth=5, verbose=0)\n",
    "dt = DecisionTreeClassifier(max_depth=8)\n",
    "knn = KNeighborsClassifier(n_neighbors = 2)\n",
    "svm = SVC(kernel='linear', C=0.025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "将pandas转换为arrays："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create Numpy arrays of train, test and target (Survived) dataframes to feed into our models\n",
    "x_train = titanic_train_data_X.values # Creates an array of the train data\n",
    "x_test = titanic_test_data_X.values \n",
    "# Creats an array of the test data\n",
    "y_train = titanic_train_data_Y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our OOF train and test predictions. These base results will be used as new features\n",
    "rf_oof_train, rf_oof_test = get_out_fold(rf, x_train, y_train, x_test) # Random Forest\n",
    "ada_oof_train, ada_oof_test = get_out_fold(ada, x_train, y_train, x_test) # AdaBoost \n",
    "et_oof_train, et_oof_test = get_out_fold(et, x_train, y_train, x_test) # Extra Trees\n",
    "gb_oof_train, gb_oof_test = get_out_fold(gb, x_train, y_train, x_test) # Gradient Boost\n",
    "dt_oof_train, dt_oof_test = get_out_fold(dt, x_train, y_train, x_test) # Decision Tree\n",
    "knn_oof_train, knn_oof_test = get_out_fold(knn, x_train, y_train, x_test) # KNeighbors\n",
    "svm_oof_train, svm_oof_test = get_out_fold(svm, x_train, y_train, x_test) # Support Vector\n",
    "print(\"Training is complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(4) 预测并生成提交文件\n",
    "\n",
    "Level 2：\n",
    "\n",
    "我们利用XGBoost，使用第一层预测的结果作为特征对最终的结果进行预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train = np.concatenate((rf_oof_train, ada_oof_train, et_oof_train, gb_oof_train, dt_oof_train, knn_oof_train, svm_oof_train), axis=1)\n",
    "x_test = np.concatenate((rf_oof_test, ada_oof_test, et_oof_test, gb_oof_test, dt_oof_test, knn_oof_test, svm_oof_test), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "gbm = XGBClassifier( n_estimators= 2000, max_depth= 4, min_child_weight= 2, gamma=0.9, subsample=0.8, \n",
    "                     colsample_bytree=0.8, objective= 'binary:logistic', nthread= -1, scale_pos_weight=1).fit(x_train, y_train)\n",
    "predictions = gbm.predict(x_test)\n",
    "StackingSubmission = pd.DataFrame({'PassengerId': PassengerId, 'Survived': predictions})\n",
    "StackingSubmission.to_csv('StackingSubmission.csv',index=False,sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "7. 验证：学习曲线\n",
    "在我们对数据不断地进行特征工程，产生的特征越来越多，用大量的特征对模型进行训练，会使我们的训练集拟合得越来越好，但同时也可能会逐渐丧失泛化能力，从而在测试数据上表现不佳，发生过拟合现象。\n",
    "\n",
    "当然我们建立的模型可能不仅在预测集上表型不好，也很可能是因为在训练集上的表现就不佳，处于欠拟合状态。\n",
    "\n",
    "下图是在吴恩达老师的机器学习课程上给出的四种学习曲线："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "所以我们通过学习曲线观察模型处于什么样的状态。从而决定对模型进行如何的操作。当然，我们把验证放到最后，并不是是这一步是在最后去做。对于我们的Stacking框架中第一层的各个基学习器我们都应该对其学习曲线进行观察，从而去更好地调节超参数，进而得到更好的最终结果。\n",
    "\n",
    "构建绘制学习曲线的函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.learning_curve import learning_curve\n",
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5), verbose=0):\n",
    "    \"\"\"    Generate a simple plot of the test and traning learning curve.    Parameters    ----------    estimator : object type that implements the \"fit\" and \"predict\" methods        An object of that type which is cloned for each validation.    title : string        Title for the chart.    X : array-like, shape (n_samples, n_features)        Training vector, where n_samples is the number of samples and        n_features is the number of features.    y : array-like, shape (n_samples) or (n_samples, n_features), optional        Target relative to X for classification or regression;        None for unsupervised learning.    ylim : tuple, shape (ymin, ymax), optional        Defines minimum and maximum yvalues plotted.    cv : integer, cross-validation generator, optional        If an integer is passed, it is the number of folds (defaults to 3).        Specific cross-validation objects can be passed, see        sklearn.cross_validation module for the list of possible objects    n_jobs : integer, optional        Number of jobs to run in parallel (default 1).    \"\"\"\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = x_trainY = y_train# RandomForest\n",
    "rf_parameters = {'n_jobs': -1, 'n_estimators': 500, 'warm_start': True, 'max_depth': 6, 'min_samples_leaf': 2, \n",
    "              'max_features' : 'sqrt','verbose': 0}# AdaBoost\n",
    "ada_parameters = {'n_estimators':500, 'learning_rate':0.1}# ExtraTrees\n",
    "et_parameters = {'n_jobs': -1, 'n_estimators':500, 'max_depth': 8, 'min_samples_leaf': 2, 'verbose': 0}# GradientBoosting\n",
    "gb_parameters = {'n_estimators': 500, 'max_depth': 5, 'min_samples_leaf': 2, 'verbose': 0}# DecisionTree\n",
    "dt_parameters = {'max_depth':8}# KNeighbors\n",
    "knn_parameters = {'n_neighbors':2}# SVM\n",
    "svm_parameters = {'kernel':'linear', 'C':0.025}# XGB\n",
    "gbm_parameters = {'n_estimators': 2000, 'max_depth': 4, 'min_child_weight': 2, 'gamma':0.9, 'subsample':0.8, \n",
    "               'colsample_bytree':0.8, 'objective': 'binary:logistic', 'nthread':-1, 'scale_pos_weight':1}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "title = \"Learning Curves\"\n",
    "plot_learning_curve(RandomForestClassifier(**rf_parameters), title, X, Y, cv=None,  n_jobs=4, train_sizes=[50, 100, 150, 200, 250, 350, 400, 450, 500])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "由上面的分析我们可以看出，对于RandomForest的模型，这里是存在一定的问题的，所以我们需要去调整模型的超参数，从而达到更好的效果。\n",
    "\n",
    "  8. 超参数调试\n",
    "将生成的提交文件到Kaggle提交，得分结果：\n",
    "\n",
    "xgboost stacking：0.78468；\n",
    "voting bagging ：0.79904；\n",
    "这也说明了我们的stacking模型还有很大的改进空间。所以我们可以在以下几个方面进行改进，提高模型预测的精度：\n",
    "\n",
    "特征工程：寻找更好的特征、删去影响较大的冗余特征；\n",
    "模型超参数调试：改进欠拟合或者过拟合的状态；\n",
    "改进模型框架：对于stacking框架的各层模型进行更好的选择；\n",
    "调参的过程.............................................慢慢尝试吧。\n",
    "\n",
    "  参考引用：\n",
    "分分钟带你杀入Kaggle Top 1%（http://t.cn/RoXGHto）\n",
    "机器学习实战之Kaggle_Titanic预测（http://t.cn/RYd3Xlu）\n",
    "泰坦尼克号乘客数据分析（http://t.cn/RYd3HCK）\n",
    "Kaggle泰坦尼克号生存模型——250个特征量的融合模型，排名8%（http://t.cn/RYd31l9）\n",
    "Introduction to Ensembling/Stacking in Python（http://t.cn/ROI2HyK）\n",
    "A Journey through Titanic（http://t.cn/RYd1mij）\n",
    "（完）"
   ]
  }
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