Titanic - Machine Learning from Disaster.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re as re\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('./train.csv', header = 0, dtype={'Age': np.float64})\n",
    "test = pd.read_csv('./test.csv', header = 0, dtype={'Age': np.float64})\n",
    "full_data = [train, test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(train.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp       Parch        Fare\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 获救情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc79f940>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.Survived.value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 乘客等级分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '$Cabin\\\\ level$')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.Pclass.value_counts().plot(kind = 'bar')\n",
    "plt.ylabel(u'people')\n",
    "plt.title(u'$Cabin\\ level$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'age-Survied (1:survived)')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.Survived, train.Age)\n",
    "plt.ylabel(u'Age')\n",
    "plt.grid(b=True, which='major', axis='y')\n",
    "plt.title(u'age-Survied (1:survived)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xa6c5550>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.Age[train.Pclass == 1].plot(kind = 'kde')\n",
    "train.Age[train.Pclass == 2].plot(kind = 'kde')\n",
    "train.Age[train.Pclass == 3].plot(kind = 'kde')\n",
    "plt.xlabel(u'age')\n",
    "plt.ylabel(u'p')\n",
    "plt.title(u'cabin_level-age')\n",
    "plt.legend((u'level = 0', u'level = 1',u'level = 2'),loc=\"best\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa6f84a8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2+HBV3TfNeTSaJBuBj7Lyr+mDrHxq7ptZWXZ7W1X95xTHmyqDXlJXklwF/AwrH5n+cFUdmPJIU2fQS1Ln/GOsJHXOoJekzhn0ktQ5g16SOmfQS1Ln/hcCo2pwtnZoawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.Embarked.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 不同等级的乘客获救的情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa777940>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Survived0 = train.Pclass[train.Survived == 0].value_counts() # 没有存活的\n",
    "Survived1 = train.Pclass[train.Survived == 1].value_counts() # 存活的\n",
    "df = pd.DataFrame({u'survived': Survived0, u'not survived': Survived1})\n",
    "df.plot(kind='bar')\n",
    "df.plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 各个性别获救情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Survived_m = train.Survived[train.Sex == 'male'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Survived_f = train.Survived[train.Sex == 'female'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({u'male' : Survived_m, u'female': Survived_f})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar', stacked=True)\n",
    "plt.xlabel(u'sex')\n",
    "plt.ylabel(u'people')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 各种级别情况下个性别的获救情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xs4/NrCj4uSRqmV+Z2SYz22Bmo8J4ACIi0nB1/o/c41QA/+qce9/M0oFCM3snuO8/nHNTomc2s17A9UBv4GzgH2Z2jnOuMhEdFxGR2DX4SN85t8M5934wXQasAzrXscgVwCvOucPOua3AJmBQYzorIiKNE9eYvpllALnA8qDpZ2a22sz+ZGbtgrbOwLaoxUqp5UXCzCaY2UYz211SUhJPl6QWqm14VNtwqb7hiDn0zaw18Bpwj3PuC+BZoAfQD9gB/HvVrDUs7mpap3PuOedclnOuQ7du3WLtktRBtQ2Pahsu1TccMYW+mTUnEvgvOef+BuCc2+Wcq3TOHQVm8NUQTinQNWrxLsD2xndZRETiFcvVOwY8D6xzzk2Nau8UNdtVwJpgej5wvZmdZmaZQBaQ3/gui4hIvGK5eucC4MfAB2ZWFLQ9ANxgZv2IDN0UA3cAOOc+NLM5wFoiV/5M1JU7IiJNq8Gh75xbSs3j9AvqWGYSMCmOfomISAj0iVwREY8o9EVEPKLQFxHxiEJfRMQjCn0REY8o9EVEPKLQFxHxiEJfRMQjCn0REY8o9EVEPKLQFxHxiEJfRMQjCn0REY8o9EVEPKLQFxHxiEJfRMQjCn0REY8o9EVEPKLQFxHxiEJfRMQjCn0REY8o9EVEPKLQFxHxiEJfRMQjCn0REY80OPTNrKuZLTKzdWb2oZndHbR/w8zeMbONwe92QbuZ2VNmtsnMVptZ/7AehIiINEwsR/oVwL86584DhgATzawXcD+w0DmXBSwMbgP8AMgKfiYAzyas1yIiEpcGh75zbodz7v1gugxYB3QGrgBeCGZ7AbgymL4CeNFFLAPamlmnhPVcRERiFteYvpllALnAcuCbzrkdEHlhADoGs3UGtkUtVhq0iYhIE4k59M2sNfAacI9z7ou6Zq2hzdWyzgnBOYHdJSUlsXZJ6qDahke1DZfqG46YQt/MmhMJ/Jecc38LmndVDdsEvz8J2kuBrlGLdwG217Re59xzzrks51yHbt26xdIlqYdqGx7VNlyqbzhiuXrHgOeBdc65qVF3zQfGBdPjgHlR7TcHV/EMAfZVDQOJiEjTaBbDvBcAPwY+MLOioO0BYDIwx8x+ApQAY4P7FgCXAJuAA8AtCemxiIjErcGh75xbSs3j9AAja5jfARPj7JeIiIRAn8gVEfGIQl9ExCMKfRERjyj0RUQ8otAXEfGIQl9ExCMKfRERjyj0RUQ8otAXEfGIQl9ExCMKfRERjyj0RUQ8otAXEfGIQl9ExCMKfRERjyj0RUQ8otAXEfFILP8uUUQaIeP+NxO2ruLJlyZsXeIXHemLiHhEoS8i4hGFvoiIRxT6IiIeOSVP5CbqhJhOhomIb3SkLyLiEYW+iIhHFPoiIh6JKfTN7E9m9omZrYlqe8TMPjazouDnkqj7fmVmm8xsg5mNSmTHRUQkdrEe6c8CRtfQ/h/OuX7BzwIAM+sFXA/0Dpb5g5mlNqazIiLSODGFvnPuXeCzBs5+BfCKc+6wc24rsAkYFGP/REQkgRI1pv8zM1sdDP+0C9o6A9ui5ikN2k5gZhPMbKOZ7S4pKUlQlwRU2zCptuFSfcORiNB/FugB9AN2AP8etFsN87qaVuCce845l+Wc69CtW7cEdEmqqLbhUW3DpfqGo9Gh75zb5ZyrdM4dBWbw1RBOKdA1atYuwPbGbk9EROLX6NA3s05RN68Cqq7smQ9cb2anmVkmkAXkN3Z7IiISv5i+hsHM/gKMANqbWSnwMDDCzPoRGbopBu4AcM59aGZzgLVABTDROVeZuK6LiEisYgp959wNNTQ/X8f8k4BJsXZKRETCoU/kioh4RKEvIuIRhb6IiEcU+iIiHlHoi4h4RKEvIuIRhb6IiEcU+iIiHlHoi4h4RKEvIuIRhb6IiEcU+iIiHlHoi4h4RKEvIuIRhb6IiEcU+iIiHlHoi4h4RKEvIuIRhb6IiEcU+iIiHlHoi4h4RKEvIuIRhb6IiEcU+iIiHlHoi4h4JKbQN7M/mdknZrYmqu0bZvaOmW0MfrcL2s3MnjKzTWa22sz6J7rzIiISm1iP9GcBo49rux9Y6JzLAhYGtwF+AGQFPxOAZ+PvpoiIJEJMoe+cexf47LjmK4AXgukXgCuj2l90EcuAtmbWqTGdFRGRxknEmP43nXM7AILfHYP2zsC2qPlKgzYREWkiYZ7ItRraXI0zmk0IzgnsLikpCbFL/lFtw6Pahkv1DUciQn9X1bBN8PuToL0U6Bo1Xxdge00rcM4955zLcs516NatWwK6JFVU2/CotuFSfcORiNCfD4wLpscB86Labw6u4hkC7KsaBhIRkabRLJaZzewvwAigvZmVAg8Dk4E5ZvYToAQYG8y+ALgE2AQcAG5JUJ9FRCROMYW+c+6GWu4aWcO8DpgYT6dERCQc+kSuiIhHFPoiIh5R6IuIeEShLyLiEYW+iIhHFPoiIh5R6IuIeEShLyLiEYW+iIhHFPoiIh5R6IuIeEShLyLiEYW+iIhHFPoiIh6J6auV5esv4/43E7Ke4smXJmQ9ieoPJK5PIqcyHemLiHhEoS8i4hGFvoiIRxT6IiIeUeiLiHhEoS8i4hFdsinisWS7RFfCpyN9ERGPKPRFRDyi0BcR8YhCX0TEIwk7kWtmxUAZUAlUOOcGmtk3gNlABlAMXOuc25uobYqISGwSfaR/kXOun3NuYHD7fmChcy4LWBjcFhGRJhL28M4VwAvB9AvAlSFvT0RE6pDI0HfA22ZWaGYTgrZvOud2AAS/O9a0oJlNMLONZra7pKQkgV0S1TY8qm24VN9wJDL0L3DO9Qd+AEw0s7yGLuice845l+Wc69CtW7cEdklU2/CotuFSfcORsBO5zrntwe9PzGwuMAjYZWadnHM7zKwT8EmiticiksyS9dPOCTnSN7NWZpZeNQ18H1gDzAfGBbONA+YlYnsiIhKfRB3pfxOYa2ZV63zZOfd3M1sBzDGznwAlwNgEbU9EROKQkNB3zm0B+tbQvgcYmYhtiIhI4+kTuSIiHlHoi4h4RKEvIuIRhb6IiEcU+iIiHlHoi4h4RKEvIuIRhb6IiEcU+iIiHlHoi4h4RKEvIuIRhb6IiEcU+iIiHlHoi4h4RKEvIuIRhb6IiEcU+iIiHlHoi4h4RKEvIuIRhb6IiEcU+iIiHlHoi4h4RKEvIuKRZk3dga+DjPvfTMh6iidfmpD1iIjURkf6IiIeCT30zWy0mW0ws01mdn/Y2xMRkdqFGvpmlgr8HvgB0Au4wcx6hblNERGpXdhH+oOATc65Lc65L4FXgCtC3qaIiNQi7NDvDGyLul0atImISBMw51x4KzcbC4xyzt0W3P4xMMg59y/HzTcBuBdoC7QGPkzA5tsDnyZgPYmSqP50d851aOjMqm3MGlxfT2oLeu6G6aTXNuzQHwo84pwbFdz+FYBz7onQNvrVtguccwPD3k5DJVt/GiPZHkuy9acxkvGxJGOf4pVsj6Up+hP28M4KIMvMMs2sBXA9MD/kbYqISC1C/XCWc67CzH4G/F8gFfiTcy4Rb9FERCQOoX8i1zm3AFgQ9nZq8FwTbLMuydafxki2x5Js/WmMZHwsydineCXbYznp/Ql1TF9ERJKLvoZBRMQjX9vQN7PLE/W1D2ZWnoj11LDe8WZ2dhjrDpNqGy7VNzyq7Sk+vGNmzZxzFSdhO+XOudYhrHcx8EvnXEGi191Yqm24VN/wqLb1cM41+Q/QCngTWAWsAa4DioH2wf0DgcXB9CNETn68DbwMLAd6R61rMTAAGA88A7QJ1pUS3H86kU8JNwd6AH8HCoElQM9gnkzgPSKXnD4OlDfwcWQA64AZRD5I8jbQEugHLANWA3OBdsAPgXJgA1AEtFRtT63aqr567p6KtQ3lDyGOnXsNMCPqdtUOqW3nFlYVA/g58Ggw3Qn4n2B6PPBMMD0PuCiYvg74YzC9EMgKpgcD/xVMzwduDqYnxrhzK4B+we05wE3BTr0waHsMmBb1RByo2p6atVV99dw9FWubLGP6HwDfNbN/M7Phzrl99cw/3zl3MJieA4wNpq8FXq1h/tlEdipEPiA228xaA8OAV82sCPg/RJ4cABcAfwmm/xzjY9nqnCsKpguJHDW0dc79v6DtBSAvxnU2hmobLtU3PKptCJLiP2c55/7HzAYAlwBPmNnbRF4Zq16U0o5bZH/Ush+b2R4zyyGyA++oYRPzg/V+g8hbvP8i8tbxc+dcv9q6FefDORw1XUnke0OajGobLtU3PKptOJLiSD84U33AOfefwBSgP5G3cQOCWa6pZxWvAPcBbZxzHxx/p3OuHMgHngTecM5VOue+ALYGXwqHRfQNFvlvIq/8ADfG/cAi9gF7zWx4cPvHQNWrexmQ3sj110m1DZfqGx7VNhxJEfpAHyA/eDv1IPAb4FHgSTNbQuSVsS5/JbIz5tQxz2wi42izo9puBH5iZquInGCp+q7/u4GJZraCyDhiY40D/reZrSZy8uaxoH0WMN3MisysZQK2UxPVNrzaguqr5278mqS2p/QlmyIiEptkOdIXEZGTQKEvIuIRhb6IiEcU+iIiHlHoi4h4RKEvIuIRhb6IiEcU+iIiHvn/30uXL0KCfW4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(1, 4, 1)\n",
    "train.Survived[train.Sex == 'female'][train.Pclass != 3].value_counts().plot(kind='bar', \n",
    "                                                            label=u'female high')\n",
    "ax1.set_xticklabels([u'survived', u'not'], rotation=0)\n",
    "plt.legend([u'female-high'], loc='best')\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(1, 4, 2, sharey=ax1)\n",
    "train.Survived[train.Sex == 'female'][train.Pclass == 3].value_counts().plot(kind='bar', \n",
    "                                                                             label=u'female-low')\n",
    "ax2.set_xticklabels([u'survived', u'not'], rotation=0)\n",
    "plt.legend([u'female-low'], loc='best')\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(1, 4, 3, sharey=ax1)\n",
    "train.Survived[train.Sex == 'male'][train.Pclass != 3].value_counts().plot(kind='bar', \n",
    "                                                            label=u'male, high')\n",
    "ax3.set_xticklabels([u'survived', u'not'], rotation=0)\n",
    "plt.legend([u'male-high'], loc='best')\n",
    "\n",
    "\n",
    "ax4 = fig.add_subplot(1, 4, 4, sharey=ax1)\n",
    "train.Survived[train.Sex == 'male'][train.Pclass == 3].value_counts().plot(kind='bar', \n",
    "                                                            label=u'male, low')\n",
    "ax4.set_xticklabels([u'survived', u'not'], rotation=0)\n",
    "plt.legend([u'male-low'], loc='best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* PassengerId => 乘客 ID\n",
    "* Pclass => 乘客等级(1/2/3 等舱位)\n",
    "* Name => 乘客姓名\n",
    "* Sex => 性别\n",
    "* Age => 年龄 \n",
    "* SibSp => 堂兄弟/妹个数\n",
    "* Parch => 父母与小孩个数\n",
    "* Ticket => 船票信息\n",
    "* Fare => 票价\n",
    "* Cabin => 客舱\n",
    "* Embarked => 登船港口\n",
    "* Survived => 是否获救 => label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程\n",
    "## 以下是计算家庭的大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in full_data:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   FamilySize  Survived\n",
      "0           1  0.303538\n",
      "1           2  0.552795\n",
      "2           3  0.578431\n",
      "3           4  0.724138\n",
      "4           5  0.200000\n",
      "5           6  0.136364\n",
      "6           7  0.333333\n",
      "7           8  0.000000\n",
      "8          11  0.000000\n"
     ]
    }
   ],
   "source": [
    "print(train[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IsAlone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   IsAlone  Survived\n",
      "0        0  0.505650\n",
      "1        1  0.303538\n"
     ]
    }
   ],
   "source": [
    "for dataset in full_data:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[train['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "print(train[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Embarked  Survived\n",
      "0        C  0.553571\n",
      "1        Q  0.389610\n",
      "2        S  0.339009\n"
     ]
    }
   ],
   "source": [
    "for dataset in full_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna('S')\n",
    "print(train[['Embarked', 'Survived']].groupby(['Embarked'], as_index = False).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   CategoricalFare  Survived\n",
      "0   (-0.001, 7.91]  0.197309\n",
      "1   (7.91, 14.454]  0.303571\n",
      "2   (14.454, 31.0]  0.454955\n",
      "3  (31.0, 512.329]  0.581081\n"
     ]
    }
   ],
   "source": [
    "for dataset in full_data:\n",
    "    dataset['Fare'] = dataset['Fare'].fillna(train['Fare'].median()) # 为何使用median\n",
    "train['CategoricalFare'] = pd.qcut(train['Fare'], 4)\n",
    "print(train[['CategoricalFare', 'Survived']].groupby(['CategoricalFare'], as_index=False).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "for dataset in full_data:\n",
    "    age_avg = dataset['Age'].mean()\n",
    "    age_std = dataset['Age'].std()\n",
    "    age_null_count = dataset['Age'].isnull().sum()\n",
    "    age_null_random_list = np.random.randint(age_avg - age_std, age_avg+age_std, size=age_null_count)\n",
    "    dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list\n",
    "    dataset['Age'] = dataset['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  CategoricalAge  Survived\n",
      "0  (-0.08, 16.0]  0.521368\n",
      "1   (16.0, 32.0]  0.365517\n",
      "2   (32.0, 48.0]  0.351351\n",
      "3   (48.0, 64.0]  0.434783\n",
      "4   (64.0, 80.0]  0.090909\n"
     ]
    }
   ],
   "source": [
    "train['CategoricalAge'] = pd.cut(train['Age'], 5)\n",
    "print(train[['CategoricalAge', 'Survived']].groupby(['CategoricalAge'], as_index=False).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 16 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 int32\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           891 non-null object\n",
      "FamilySize         891 non-null int64\n",
      "IsAlone            891 non-null int64\n",
      "CategoricalFare    891 non-null category\n",
      "CategoricalAge     891 non-null category\n",
      "dtypes: category(2), float64(1), int32(1), int64(7), object(5)\n",
      "memory usage: 95.9+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "def get_title(name):\n",
    "    title_search = re.search(' ([A-Za-z+]+)\\.', name)\n",
    "    if title_search:\n",
    "        return title_search.group(1)\n",
    "    return \"\"\n",
    "\n",
    "for dataset in full_data:\n",
    "    dataset['Title'] = dataset['Name'].apply(get_title)\n",
    "\n",
    "print(pd.crosstab(train['Title'], train['Sex']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Title  Survived\n",
      "0  Master  0.575000\n",
      "1    Miss  0.702703\n",
      "2      Mr  0.156673\n",
      "3     Mrs  0.793651\n",
      "4    Rare  0.347826\n"
     ]
    }
   ],
   "source": [
    "for dataset in full_data:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col',\n",
    "                                            'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'],\n",
    "                                           'Rare')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "\n",
    "print(train[['Title', 'Survived']].groupby(['Title'], as_index=False).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sex mapping\n",
    "for dataset in full_data:\n",
    "    dataset['Sex'] = dataset['Sex'].map({'female': 0, 'male':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Title mapping\n",
    "for dataset in full_data:\n",
    "    title_mapping = {'Mr':1, 'Miss':2, 'Mrs':3, 'Master':4, 'Rare':5}\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Embarked mapping\n",
    "for dataset in full_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map({'S':0, 'C': 1, 'Q': 2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fare mapping\n",
    "for dataset in full_data:\n",
    "    dataset.loc[dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2\n",
    "    dataset.loc[dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Age mapping\n",
    "for dataset in full_data:\n",
    "    dataset.loc[dataset['Age'] <= 16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[dataset['Fare'] > 64, 'Age'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特征选择\n",
    "drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp', 'Parch', 'FamilySize']\n",
    "train = train.drop(drop_elements, axis=1)\n",
    "train = train.drop(['CategoricalAge', 'CategoricalFare'], axis = 1)\n",
    "test = test.drop(drop_elements, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Survived  Pclass  Sex  Age  Fare  Embarked  IsAlone  Title\n",
      "0         0       3    1    1     0         0        0      1\n",
      "1         1       1    0    2     3         1        0      3\n",
      "2         1       3    0    1     1         0        1      2\n",
      "3         1       1    0    2     3         0        0      3\n",
      "4         0       3    1    2     1         0        1      1\n",
      "5         0       3    1    0     1         2        1      1\n",
      "6         0       1    1    3     3         0        1      1\n",
      "7         0       3    1    0     2         0        0      4\n",
      "8         1       3    0    1     1         0        0      3\n",
      "9         1       2    0    0     2         1        0      3\n"
     ]
    }
   ],
   "source": [
    "print(train.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分类模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 216, got 192\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.metrics import accuracy_score, log_loss\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分类器\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(3),\n",
    "    SVC(probability=True),\n",
    "    DecisionTreeClassifier(),\n",
    "    RandomForestClassifier(),\n",
    "    AdaBoostClassifier(),\n",
    "    GradientBoostingClassifier(),\n",
    "    GaussianNB(),\n",
    "    LinearDiscriminantAnalysis(),\n",
    "    QuadraticDiscriminantAnalysis(),\n",
    "    LogisticRegression(),\n",
    "    XGBClassifier()\n",
    "]\n",
    "\n",
    "log_cols = ['Classifier', 'Accuracy']\n",
    "log = pd.DataFrame(columns=log_cols)\n",
    "\n",
    "sss = StratifiedShuffleSplit(n_splits=10, test_size=0.1, random_state=0)\n",
    "\n",
    "X = train[0::, 1::]\n",
    "y = train[0::, 0]\n",
    "\n",
    "acc_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "for train_index, test_index in sss.split(X, y):\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    \n",
    "    for clf in classifiers:\n",
    "        name = clf.__class__.__name__\n",
    "        clf.fit(X_train, y_train)\n",
    "        train_predictions = clf.predict(X_test)\n",
    "        acc = accuracy_score(y_test, train_predictions)\n",
    "        if name in acc_dict:\n",
    "            acc_dict[name] += acc\n",
    "        else:\n",
    "            acc_dict[name] = acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "for clf in acc_dict:\n",
    "    acc_dict[clf] = acc_dict[clf] / 10.0\n",
    "    log_entry = pd.DataFrame([[clf, acc_dict[clf]]], columns=log_cols)\n",
    "    log = log.append(log_entry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xed70fd0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Classfier Accuracy')\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x='Accuracy', y='Classifier', data=log, color=\"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0\n",
      " 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 1 0\n",
      " 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0\n",
      " 1 1 1 1 0 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0\n",
      " 0 0 1 0 0 0 0 0 1 1 0 1 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 0 1\n",
      " 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0\n",
      " 1 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 1\n",
      " 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 0 0\n",
      " 1 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0\n",
      " 1 0 0 0 0 0 1 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 1 0\n",
      " 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0\n",
      " 0 1 1 1 1 1 0 1 0 0 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "candidate_classifier = SVC()\n",
    "candidate_classifier.fit(train[0::, 1::], train[0::, 0])\n",
    "result = candidate_classifier.predict(test)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型融合\n",
    "查看特征相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdf96470>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap = plt.cm.viridis\n",
    "plt.figure(figsize=(12,12))\n",
    "train_temp = pd.read_csv('./titanic/train.csv', header = 0, dtype={'Age': np.float64})\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "sns.heatmap(train_temp.corr(), linewidths=0.1, vmax=1.0, square=True, cmap=colormap\n",
    "           , linecolor='white', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_temp = pd.read_csv('./titanic/test.csv', header = 0, dtype={'Age': np.float64})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<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",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(test_temp.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "418"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_temp = pd.DataFrame(data=result, columns=['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_temp['PassengerId'] = test_temp['PassengerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "order =['PassengerId', 'Survived']\n",
    "result_temp = result_temp[order]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_temp.to_csv('./titanic/result.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}