99_Other_Rex/XGB_0.80382.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anaconda\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn import cross_validation\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      "Age            1046 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 132.9+ KB\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "data_train  = pd.read_csv(r'\\Users\\Rex\\Desktop\\python\\train.csv',encoding='big5')\n",
    "data_test  = pd.read_csv(r'\\Users\\Rex\\Desktop\\python\\test.csv',encoding='big5')\n",
    "data=data_train.append(data_test)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
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       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin1</th>\n",
       "      <th>Cabin2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
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       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <th>1</th>\n",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
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       "      <td>PC 17599</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
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       "      <td>26.0</td>\n",
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       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>F</td>\n",
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       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>4</td>\n",
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       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>F</td>\n",
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      ],
      "text/plain": [
       "    Age Cabin Embarked     Fare  \\\n",
       "0  22.0   NaN        S   7.2500   \n",
       "1  38.0   C85        C  71.2833   \n",
       "2  26.0   NaN        S   7.9250   \n",
       "3  35.0  C123        S  53.1000   \n",
       "4  35.0   NaN        S   8.0500   \n",
       "\n",
       "                                                Name  Parch  PassengerId  \\\n",
       "0                            Braund, Mr. Owen Harris      0            1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                             Heikkinen, Miss. Laina      0            3   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)      0            4   \n",
       "4                           Allen, Mr. William Henry      0            5   \n",
       "\n",
       "   Pclass     Sex  SibSp  Survived            Ticket Cabin1 Cabin2  \n",
       "0       3    male      1       0.0         A/5 21171      F      F  \n",
       "1       1  female      1       1.0          PC 17599      C      A  \n",
       "2       3  female      0       1.0  STON/O2. 3101282      F      F  \n",
       "3       1  female      1       1.0            113803      C      A  \n",
       "4       3    male      0       0.0            373450      F      F  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Fare'] = data['Fare'].fillna(data['Fare'].mean())\n",
    "data['Age'] = data['Age'].fillna(data['Age'].median())\n",
    "data['Embarked'].fillna('S',inplace = True)\n",
    "data['Cabin1'] = data['Cabin'].str.slice(0,1)\n",
    "data.loc[data['Pclass']==1, 'Cabin2'] = 'A'\n",
    "data.loc[data['Pclass']==2, 'Cabin2'] = 'E'\n",
    "data.loc[data['Pclass']==3, 'Cabin2'] = 'F'\n",
    "data['Cabin1'].fillna(data['Cabin2'],inplace = True)\n",
    "#data['Cabin2'] = data.apply(lambda x: 'F' if pd.isnull(x['Cabin1']) and x['Pclass']==3 else x['Cabin1'], axis=1)\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 14 columns):\n",
      "Age            1309 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1309 non-null object\n",
      "Fare           1309 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "Cabin1         1309 non-null object\n",
      "Cabin2         1309 non-null object\n",
      "dtypes: float64(3), int64(4), object(7)\n",
      "memory usage: 153.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A5', 'PC', 'STONO2', 'X', 'PP', 'CA', 'SCParis', 'SCA4', 'A4',\n",
       "       'SP', 'SOC', 'WC', 'SOTONOQ', 'WEP', 'STONO', 'C', 'SCPARIS',\n",
       "       'SOP', 'Fa', 'LINE', 'FCC', 'SWPP', 'SCOW', 'PPP', 'SC', 'SCAH',\n",
       "       'AS', 'SOPP', 'FC', 'SOTONO2', 'CASOTON', 'SCA3', 'STONOQ', 'AQ4',\n",
       "       'A', 'LP', 'AQ3'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Ticket_info'] = data['Ticket'].apply(lambda x : x.replace(\".\",\"\").replace(\"/\",\"\").strip().split(' ')[0] if  x.isdigit() else 'X')\n",
    "data['Ticket_info'].head()\n",
    "data['Ticket2'] = data['Ticket'].apply(lambda x : x.replace(\".\",\"\").replace(\"/\",\"\").strip().split(' ')[0] if not x.isdigit() else 'X')\n",
    "data['Ticket2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>C</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>STON/O2. 3101282</td>\n",
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       "      <td>X</td>\n",
       "      <td>STONO2</td>\n",
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       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>113803</td>\n",
       "      <td>X</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>373450</td>\n",
       "      <td>X</td>\n",
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       "      <th>5</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330877</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>330877</td>\n",
       "      <td>X</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54.0</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17463</td>\n",
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       "      <td>17463</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>21.0750</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
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       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349909</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>349909</td>\n",
       "      <td>X</td>\n",
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       "      <th>8</th>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>347742</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>347742</td>\n",
       "      <td>X</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>237736</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>237736</td>\n",
       "      <td>X</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>G</td>\n",
       "      <td>F</td>\n",
       "      <td>X</td>\n",
       "      <td>PP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>58.0</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113783</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>113783</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>X</td>\n",
       "      <td>A5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347082</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>347082</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350406</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>350406</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>248706</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>248706</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>382652</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>382652</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>244373</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>244373</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>345763</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>345763</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2649</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>2649</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Cabin Embarked     Fare  \\\n",
       "0   22.0   NaN        S   7.2500   \n",
       "1   38.0   C85        C  71.2833   \n",
       "2   26.0   NaN        S   7.9250   \n",
       "3   35.0  C123        S  53.1000   \n",
       "4   35.0   NaN        S   8.0500   \n",
       "5   28.0   NaN        Q   8.4583   \n",
       "6   54.0   E46        S  51.8625   \n",
       "7    2.0   NaN        S  21.0750   \n",
       "8   27.0   NaN        S  11.1333   \n",
       "9   14.0   NaN        C  30.0708   \n",
       "10   4.0    G6        S  16.7000   \n",
       "11  58.0  C103        S  26.5500   \n",
       "12  20.0   NaN        S   8.0500   \n",
       "13  39.0   NaN        S  31.2750   \n",
       "14  14.0   NaN        S   7.8542   \n",
       "15  55.0   NaN        S  16.0000   \n",
       "16   2.0   NaN        Q  29.1250   \n",
       "17  28.0   NaN        S  13.0000   \n",
       "18  31.0   NaN        S  18.0000   \n",
       "19  28.0   NaN        C   7.2250   \n",
       "\n",
       "                                                 Name  Parch  PassengerId  \\\n",
       "0                             Braund, Mr. Owen Harris      0            1   \n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                              Heikkinen, Miss. Laina      0            3   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)      0            4   \n",
       "4                            Allen, Mr. William Henry      0            5   \n",
       "5                                    Moran, Mr. James      0            6   \n",
       "6                             McCarthy, Mr. Timothy J      0            7   \n",
       "7                      Palsson, Master. Gosta Leonard      1            8   \n",
       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)      2            9   \n",
       "9                 Nasser, Mrs. Nicholas (Adele Achem)      0           10   \n",
       "10                    Sandstrom, Miss. Marguerite Rut      1           11   \n",
       "11                           Bonnell, Miss. Elizabeth      0           12   \n",
       "12                     Saundercock, Mr. William Henry      0           13   \n",
       "13                        Andersson, Mr. Anders Johan      5           14   \n",
       "14               Vestrom, Miss. Hulda Amanda Adolfina      0           15   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)       0           16   \n",
       "16                               Rice, Master. Eugene      1           17   \n",
       "17                       Williams, Mr. Charles Eugene      0           18   \n",
       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...      0           19   \n",
       "19                            Masselmani, Mrs. Fatima      0           20   \n",
       "\n",
       "    Pclass     Sex  SibSp  Survived            Ticket Cabin1 Cabin2  \\\n",
       "0        3    male      1       0.0         A/5 21171      F      F   \n",
       "1        1  female      1       1.0          PC 17599      C      A   \n",
       "2        3  female      0       1.0  STON/O2. 3101282      F      F   \n",
       "3        1  female      1       1.0            113803      C      A   \n",
       "4        3    male      0       0.0            373450      F      F   \n",
       "5        3    male      0       0.0            330877      F      F   \n",
       "6        1    male      0       0.0             17463      E      A   \n",
       "7        3    male      3       0.0            349909      F      F   \n",
       "8        3  female      0       1.0            347742      F      F   \n",
       "9        2  female      1       1.0            237736      E      E   \n",
       "10       3  female      1       1.0           PP 9549      G      F   \n",
       "11       1  female      0       1.0            113783      C      A   \n",
       "12       3    male      0       0.0         A/5. 2151      F      F   \n",
       "13       3    male      1       0.0            347082      F      F   \n",
       "14       3  female      0       0.0            350406      F      F   \n",
       "15       2  female      0       1.0            248706      E      E   \n",
       "16       3    male      4       0.0            382652      F      F   \n",
       "17       2    male      0       1.0            244373      E      E   \n",
       "18       3  female      1       0.0            345763      F      F   \n",
       "19       3  female      0       1.0              2649      F      F   \n",
       "\n",
       "   Ticket_info Ticket2  \n",
       "0            X      A5  \n",
       "1            X      PC  \n",
       "2            X  STONO2  \n",
       "3       113803       X  \n",
       "4       373450       X  \n",
       "5       330877       X  \n",
       "6        17463       X  \n",
       "7       349909       X  \n",
       "8       347742       X  \n",
       "9       237736       X  \n",
       "10           X      PP  \n",
       "11      113783       X  \n",
       "12           X      A5  \n",
       "13      347082       X  \n",
       "14      350406       X  \n",
       "15      248706       X  \n",
       "16      382652       X  \n",
       "17      244373       X  \n",
       "18      345763       X  \n",
       "19        2649       X  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Title1']=data['Name'].str.split('.',expand=True)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Fare</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Ticket_info</th>\n",
       "      <th>Ticket2</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>...</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Cabin1_A</th>\n",
       "      <th>Cabin1_B</th>\n",
       "      <th>Cabin1_C</th>\n",
       "      <th>Cabin1_D</th>\n",
       "      <th>Cabin1_E</th>\n",
       "      <th>Cabin1_F</th>\n",
       "      <th>Cabin1_G</th>\n",
       "      <th>Cabin1_T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>705</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>705</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>705</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>649</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare  SibSp  Parch  Pclass  Survived  PassengerId  Ticket_info  \\\n",
       "0  22.0   7.2500      1      0       3       0.0            1          705   \n",
       "1  38.0  71.2833      1      0       1       1.0            2          705   \n",
       "2  26.0   7.9250      0      0       3       1.0            3          705   \n",
       "3  35.0  53.1000      1      0       1       1.0            4           65   \n",
       "4  35.0   8.0500      0      0       3       0.0            5          649   \n",
       "\n",
       "   Ticket2  Sex_female    ...     Embarked_Q  Embarked_S  Cabin1_A  Cabin1_B  \\\n",
       "0        2           0    ...              0           1         0         0   \n",
       "1       14           1    ...              0           0         0         0   \n",
       "2       31           1    ...              0           1         0         0   \n",
       "3       36           1    ...              0           1         0         0   \n",
       "4       36           0    ...              0           1         0         0   \n",
       "\n",
       "   Cabin1_C  Cabin1_D  Cabin1_E  Cabin1_F  Cabin1_G  Cabin1_T  \n",
       "0         0         0         0         1         0         0  \n",
       "1         1         0         0         0         0         0  \n",
       "2         0         0         0         1         0         0  \n",
       "3         1         0         0         0         0         0  \n",
       "4         0         0         0         1         0         0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pre=data[['Age','Cabin1','Fare','SibSp','Parch','Pclass','Sex','Survived','Embarked','PassengerId','Ticket_info','Ticket2',\\\n",
    "              ]]\n",
    "#data_pre=data[['Age','Cabin1','Fare','family_size','Pclass','Sex','Survived','Embarked','PassengerId']]\n",
    "data_pre = pd.get_dummies(data_pre,columns=['Sex','Embarked','Cabin1'])\n",
    "data_pre['Ticket_info'] = data_pre['Ticket_info'].astype('category').cat.codes\n",
    "data_pre['Ticket2'] = data_pre['Ticket2'].astype('category').cat.codes\n",
    "#data_pre['Title1'] = data_pre['Title1'].astype('category').cat.codes\n",
    "data_pre.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Cabin1_A</th>\n",
       "      <th>Cabin1_B</th>\n",
       "      <th>Cabin1_C</th>\n",
       "      <th>Cabin1_D</th>\n",
       "      <th>Cabin1_E</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>705</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>705</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>705</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>649</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare  SibSp  Parch  Pclass  Ticket_info  Ticket2  Sex_female  \\\n",
       "0  22.0   7.2500      1      0       3          705        2           0   \n",
       "1  38.0  71.2833      1      0       1          705       14           1   \n",
       "2  26.0   7.9250      0      0       3          705       31           1   \n",
       "3  35.0  53.1000      1      0       1           65       36           1   \n",
       "4  35.0   8.0500      0      0       3          649       36           0   \n",
       "\n",
       "   Sex_male  Embarked_C  Embarked_Q  Embarked_S  Cabin1_A  Cabin1_B  Cabin1_C  \\\n",
       "0         1           0           0           1         0         0         0   \n",
       "1         0           1           0           0         0         0         1   \n",
       "2         0           0           0           1         0         0         0   \n",
       "3         0           0           0           1         0         0         1   \n",
       "4         1           0           0           1         0         0         0   \n",
       "\n",
       "   Cabin1_D  Cabin1_E  Cabin1_F  \n",
       "0         0         0         1  \n",
       "1         0         0         0  \n",
       "2         0         0         1  \n",
       "3         0         0         0  \n",
       "4         0         0         1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pre = data_pre.drop('Cabin1_T',1)\n",
    "data_pre = data_pre.drop('Cabin1_G',1)\n",
    "\n",
    "datatrain = data_pre[pd.notnull(data_pre['Survived'])].sort_values(by=['PassengerId'])\n",
    "datatest = data_pre[~pd.notnull(data_pre['Survived'])].sort_values(by=['PassengerId'])\n",
    "datatrain1 = datatrain.drop('PassengerId',1)\n",
    "#datatrain1 = datatrain1.drop('Sex_female',1)\n",
    "datatest1 =  datatest.drop('PassengerId',1)\n",
    "#datatest = datatest1.drop('Sex_female',1)\n",
    "X_feature = datatrain1.drop('Survived',1)\n",
    "y_t = datatrain['Survived']\n",
    "\n",
    "X_feature.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "857    1.0\n",
       "52     1.0\n",
       "386    0.0\n",
       "124    0.0\n",
       "578    0.0\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X_feature,y_t\\\n",
    ",test_size=0.3,random_state = 0)\n",
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GBDT = GradientBoostingClassifier(n_estimators=100,max_depth=3,learning_rate=0.1)\n",
    "GBDT.fit(X_train,y_train)\n",
    "Xgb = XGBClassifier()\n",
    "Xgb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:0.913 \n",
      "\n",
      "Classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.90      0.96      0.93       381\n",
      "        1.0       0.94      0.83      0.88       242\n",
      "\n",
      "avg / total       0.91      0.91      0.91       623\n",
      " \n",
      "\n",
      "Confusion matrix\n",
      "[[367  14]\n",
      " [ 40 202]] \n",
      "\n",
      "Accuracy:0.897 \n",
      "\n",
      "Classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.90      0.94      0.92       381\n",
      "        1.0       0.90      0.83      0.86       242\n",
      "\n",
      "avg / total       0.90      0.90      0.90       623\n",
      " \n",
      "\n",
      "Confusion matrix\n",
      "[[358  23]\n",
      " [ 41 201]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "def m_performance(X,y,clf, show_accuracy=True, show_classification_report=True, show_confusion_matrix=True):\n",
    "    y_pred=clf.predict(X)   \n",
    "    if show_accuracy:\n",
    "        print (\"Accuracy:{0:.3f}\".format(metrics.accuracy_score(y,y_pred)),\"\\n\")\n",
    "\n",
    "    if show_classification_report:\n",
    "        print (\"Classification report\")\n",
    "        print (metrics.classification_report(y,y_pred),\"\\n\")\n",
    "        \n",
    "    if show_confusion_matrix:\n",
    "        print (\"Confusion matrix\")\n",
    "        print (metrics.confusion_matrix(y,y_pred),\"\\n\")\n",
    "        \n",
    "m_performance(X_train,y_train,GBDT, show_classification_report=True, show_confusion_matrix=True)\n",
    "m_performance(X_train,y_train,Xgb, show_classification_report=True, show_confusion_matrix=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8351186454863434\n",
      "0.8339257185028515\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import cross_val_score # K折交叉验证模块\n",
    "\n",
    "#使用K折交叉验证模块\n",
    "scores = cross_val_score(GBDT, X_feature,y_t, cv=5, scoring='accuracy')\n",
    "scores1 = cross_val_score(Xgb, X_feature,y_t, cv=5, scoring='accuracy')\n",
    "#将5次的预测准确率打印出\n",
    "print(scores.mean())\n",
    "print(scores1.mean())\n",
    "# 0.973333333333\n",
    "\n",
    "#from sklearn.ensemble import RandomForestClassifier\n",
    "#forest = RandomForestClassifier(criterion='entropy',n_estimators=100,random_state =2 ,n_jobs = 2)\n",
    "#forest.fit(X_train,y_train)\n",
    "#m_performance(X_train,y_train,forest, show_classification_report=True, show_confusion_matrix=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.learning_curve import learning_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\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\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "title = \"Learning Curves (Random Forest, n_estimators = 100)\"\n",
    "cv = cross_validation.ShuffleSplit(datatrain.drop('Survived',1).shape[0], n_iter=10,test_size=0.3, random_state=0)\n",
    "plot_learning_curve(GBDT, title, X_feature,y_t, (0.0, 1.01), cv=cv, n_jobs=4)\n",
    "plot_learning_curve(Xgb, title, X_feature,y_t, (0.0, 1.01), cv=cv, n_jobs=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 418 entries, 0 to 417\n",
      "Data columns (total 18 columns):\n",
      "Age            418 non-null float64\n",
      "Fare           418 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Ticket_info    418 non-null int16\n",
      "Ticket2        418 non-null int8\n",
      "Sex_female     418 non-null uint8\n",
      "Sex_male       418 non-null uint8\n",
      "Embarked_C     418 non-null uint8\n",
      "Embarked_Q     418 non-null uint8\n",
      "Embarked_S     418 non-null uint8\n",
      "Cabin1_A       418 non-null uint8\n",
      "Cabin1_B       418 non-null uint8\n",
      "Cabin1_C       418 non-null uint8\n",
      "Cabin1_D       418 non-null uint8\n",
      "Cabin1_E       418 non-null uint8\n",
      "Cabin1_F       418 non-null uint8\n",
      "dtypes: float64(2), int16(1), int64(3), int8(1), uint8(11)\n",
      "memory usage: 25.3 KB\n"
     ]
    }
   ],
   "source": [
    "datatest123 = datatest1.drop('Survived',1)\n",
    "datatest123.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit= pd.read_csv(r'\\Users\\Rex\\Desktop\\python\\gender_submission.csv',encoding='big5')\n",
    "submit1= pd.read_csv(r'\\Users\\Rex\\Desktop\\python\\gender_submission.csv',encoding='big5')\n",
    "GBDT_res =GBDT.predict(datatest123)\n",
    "submit['Survived'] = GBDT_res\n",
    "submit['Survived'] = submit['Survived'].astype(int)\n",
    "\n",
    "Xgb_res =Xgb.predict(datatest123)\n",
    "submit1['Survived'] = Xgb_res\n",
    "submit1['Survived'] = submit1['Survived'].astype(int)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pwd\n",
    "submit.to_csv(r'\\Users\\Rex\\Desktop\\python\\submit_to_kaggle_titanic.csv', index= False)\n",
    "submit1.to_csv(r'\\Users\\Rex\\Desktop\\python\\submit_to_kaggle_titanic_xgb.csv', index= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier(n_estimators=1000, random_state=0, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=1000, n_jobs=-1,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importances</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1680</td>\n",
       "      <td>Age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1607</td>\n",
       "      <td>Ticket_info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1462</td>\n",
       "      <td>Fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.1334</td>\n",
       "      <td>Sex_male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1314</td>\n",
       "      <td>Sex_female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0508</td>\n",
       "      <td>Pclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0401</td>\n",
       "      <td>SibSp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0361</td>\n",
       "      <td>Ticket2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0327</td>\n",
       "      <td>Cabin1_F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0299</td>\n",
       "      <td>Parch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0151</td>\n",
       "      <td>Embarked_S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0125</td>\n",
       "      <td>Cabin1_E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0118</td>\n",
       "      <td>Embarked_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0084</td>\n",
       "      <td>Embarked_Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0062</td>\n",
       "      <td>Cabin1_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0061</td>\n",
       "      <td>Cabin1_D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0055</td>\n",
       "      <td>Cabin1_B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0051</td>\n",
       "      <td>Cabin1_A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    importances     features\n",
       "0        0.1680          Age\n",
       "1        0.1607  Ticket_info\n",
       "2        0.1462         Fare\n",
       "3        0.1334     Sex_male\n",
       "4        0.1314   Sex_female\n",
       "5        0.0508       Pclass\n",
       "6        0.0401        SibSp\n",
       "7        0.0361      Ticket2\n",
       "8        0.0327     Cabin1_F\n",
       "9        0.0299        Parch\n",
       "10       0.0151   Embarked_S\n",
       "11       0.0125     Cabin1_E\n",
       "12       0.0118   Embarked_C\n",
       "13       0.0084   Embarked_Q\n",
       "14       0.0062     Cabin1_C\n",
       "15       0.0061     Cabin1_D\n",
       "16       0.0055     Cabin1_B\n",
       "17       0.0051     Cabin1_A"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = sorted(zip(map(lambda x: round(x, 4), clf.feature_importances_), X_train.columns), reverse=True)\n",
    "rf_imp = pd.DataFrame(imp, columns=['importances', 'features'])\n",
    "rf_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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