Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 首先是导入数据\n",
    "import pandas as pd\n",
    "train = pd.read_csv('train.csv')\n",
    "predict = pd.read_csv('test.csv')\n",
    "# 查看数据的大概样子，知晓每一个属性的对应实际意义,函数如下\n",
    "train.head()  #用来查看一个dataframe的前几行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
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       "      <td>62.0</td>\n",
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       "      <td>9.6875</td>\n",
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       "      <td>Q</td>\n",
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       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0          892         0       3   \n",
       "1          893         1       3   \n",
       "2          894         0       2   \n",
       "3          895         0       3   \n",
       "4          896         1       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                              Kelly, Mr. James    male  34.5      0      0   \n",
       "1              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "0   330911   7.8292   NaN        Q  \n",
       "1   363272   7.0000   NaN        S  \n",
       "2   240276   9.6875   NaN        Q  \n",
       "3   315154   8.6625   NaN        S  \n",
       "4  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 舍去冗余和噪声特征\n",
    "train_new = train.drop(['PassengerId','Name','Cabin','Ticket'], axis=1)\n",
    "predict_new = predict.drop(['PassengerId','Name','Cabin','Ticket'], axis=1)\n",
    " # 给Age的缺省值指定一个默认值，这里我一拍大腿想要设定为随机值，具体为[0:99]之间的均匀分布。给Embarked的缺省值指定为众数。\n",
    "import numpy as np\n",
    "train_new['Age']=train_new['Age'].fillna(round(np.random.uniform(1,99)))\n",
    "train_new['Embarked'] = train_new['Embarked'].fillna(train['Embarked'].value_counts().argmax())\n",
    "\n",
    "# 同样地，搞定test\n",
    "predict_new['Age']=predict_new['Age'].fillna(round(np.random.uniform(1,99)))\n",
    "predict_new['Embarked'] = predict_new['Embarked'].fillna(predict['Embarked'].value_counts().argmax())\n",
    "predict_new['Fare'] = predict_new['Fare'].fillna(predict['Fare'].value_counts().argmax())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wangruidong/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:10: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/Users/wangruidong/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:27: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n"
     ]
    }
   ],
   "source": [
    "#用哑变量数值化离散的特征值,由于Sex只有两个分类，不需要进行这一步\n",
    "dummy_Pclass = pd.get_dummies(train['Pclass'], prefix='Pclass')\n",
    "dummy_Embarked = pd.get_dummies(train['Embarked'], prefix='Embarked')\n",
    "\n",
    "predict['Sex'] = predict['Sex'].replace({'female':2,'male':1})\n",
    "train['Sex'] = train['Sex'].replace({'female':2,'male':1})\n",
    "\n",
    "# 行成新的dataframe\n",
    "cols_to_keep = ['Age', 'Sex','SibSp','Parch','Fare']\n",
    "train = train[['Age','Sex', 'SibSp','Parch','Fare','Survived']].join([dummy_Pclass.ix[:, 'Pclass_2':],dummy_Embarked.ix[:,'Embarked_Q':]])\n",
    "predict = predict[cols_to_keep].join([dummy_Pclass.ix[:, 'Pclass_2':],dummy_Embarked.ix[:,'Embarked_Q':]])\n",
    "train.head() \n",
    "\n",
    "# 查看正反例数量对比，正反例出现的概率是一样的。减少因为样本选取的原因对预测结果的影响。\n",
    "train['Survived'].value_counts()\n",
    "#可以看到训练样本中正反例的数量比是：\n",
    "# 0    549\n",
    "# 1    342\n",
    "# Name: Survived, dtype: int64\n",
    "\n",
    "#平衡正反例数量，思路是先把数量多的样本的行打乱，然后取前面一部分，再和数量少的样本合并成一个新的训练集\n",
    "train_positive = train[train['Survived']==1]\n",
    "train_negative = train[train['Survived']==0]\n",
    "import numpy as n\n",
    "row_list = list(train_negative.index)\n",
    "np.random.shuffle(row_list)\n",
    "train = train_positive.append(train_negative.ix[row_list[0:len(train_positive.index)]])\n",
    "Y_train = train['Survived']\n",
    "X_train = train[['Sex','Age','SibSp','Parch','Fare','Pclass_2','Pclass_3','Embarked_Q','Embarked_S']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 10 columns):\n",
      "Age           714 non-null float64\n",
      "Sex           891 non-null int64\n",
      "SibSp         891 non-null int64\n",
      "Parch         891 non-null int64\n",
      "Fare          891 non-null float64\n",
      "Survived      891 non-null int64\n",
      "Pclass_2      891 non-null uint8\n",
      "Pclass_3      891 non-null uint8\n",
      "Embarked_Q    891 non-null uint8\n",
      "Embarked_S    891 non-null uint8\n",
      "dtypes: float64(2), int64(4), uint8(4)\n",
      "memory usage: 45.3 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-a9bbb8404af1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'l2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdual\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0001\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_intercept\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mintercept_scaling\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'liblinear'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_validation\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'f1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m#score.mean()的结果是0.77296418431066638\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m#使用该模型预测，预测结果写入'logistic_exp1.csv'中。\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)\u001b[0m\n\u001b[1;32m   1579\u001b[0m                                               \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1580\u001b[0m                                               fit_params)\n\u001b[0;32m-> 1581\u001b[0;31m                       for train, test in cv)\n\u001b[0m\u001b[1;32m   1582\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    777\u001b[0m             \u001b[0;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    778\u001b[0m             \u001b[0;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    780\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    623\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    586\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 588\u001b[0;31m         \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    109\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    330\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, error_score)\u001b[0m\n\u001b[1;32m   1673\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1675\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1676\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1677\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1215\u001b[0m         X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype,\n\u001b[0;32m-> 1216\u001b[0;31m                          order=\"C\")\n\u001b[0m\u001b[1;32m   1217\u001b[0m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    571\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    572\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 573\u001b[0;31m                     ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[1;32m    574\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    451\u001b[0m                              % (array.ndim, estimator_name))\n\u001b[1;32m    452\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m             \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m     42\u001b[0m             and not np.isfinite(X).all()):\n\u001b[1;32m     43\u001b[0m         raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 44\u001b[0;31m                          \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "# 开始逻辑回归的实验\n",
    "import sklearn.linear_model as md\n",
    "lg = md.LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=2.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='liblinear', max_iter=1000)\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "scores = cross_val_score(lg, X_train, Y_train, cv=10, scoring='f1')\n",
    "scores.mean()  #score.mean()的结果是0.77296418431066638\n",
    "#使用该模型预测，预测结果写入'logistic_exp1.csv'中。\n",
    "lg.fit(X_train,Y_train)\n",
    "result = lg.predict(X_test)\n",
    "result= pd.DataFrame(result,columns=['Survived'])\n",
    "result.insert(0,'PassengerId',test_raw['PassengerId'])\n",
    "result.to_csv('logistic_exp1.csv',index= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from matplotlib import pyplot\n",
    "import seaborn\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, learning_curve\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Read in data\n",
    "training_set = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PassengerId  Survived  Pclass  \\\n",
      "0            1         0       3   \n",
      "1            2         1       1   \n",
      "2            3         1       3   \n",
      "3            4         1       1   \n",
      "4            5         0       3   \n",
      "\n",
      "                                                Name     Sex   Age  SibSp  \\\n",
      "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
      "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
      "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
      "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
      "4                           Allen, Mr. William Henry    male  35.0      0   \n",
      "\n",
      "   Parch            Ticket     Fare Cabin Embarked  \n",
      "0      0         A/5 21171   7.2500   NaN        S  \n",
      "1      0          PC 17599  71.2833   C85        C  \n",
      "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
      "3      0            113803  53.1000  C123        S  \n",
      "4      0            373450   8.0500   NaN        S  \n",
      "Number of null values in training set:\n",
      "PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            177\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             0\n",
      "Cabin          687\n",
      "Embarked         2\n",
      "dtype: int64\n",
      "(891, 12)\n"
     ]
    }
   ],
   "source": [
    "print(training_set.head())\n",
    "\n",
    "# Check for nulls\n",
    "print(\"Number of null values in training set:\")\n",
    "print(training_set.isnull().sum())\n",
    "\n",
    "# Overall shape\n",
    "print(training_set.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of each sex who survived\n",
      "        Survived\n",
      "Sex             \n",
      "female  0.742038\n",
      "male    0.188908\n",
      "\n",
      "Fraction who survived by sex and Pclass\n",
      "Pclass         1         2         3\n",
      "Sex                                 \n",
      "female  0.968085  0.921053  0.500000\n",
      "male    0.368852  0.157407  0.135447\n",
      "\n",
      "Total number who survived vs. did not\n",
      "0    549\n",
      "1    342\n",
      "Name: Survived, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Fraction of females who survived and fraction of males who survived\n",
    "print(\"Fraction of each sex who survived\")\n",
    "print(training_set[['Sex', 'Survived']].groupby('Sex').mean())\n",
    "# The same result can be gotten using:\n",
    "# training_set.groupby('Sex')['Survived'].mean()\n",
    "# but the latter will give a Series instead of a DataFrame\n",
    "# https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.09-Pivot-Tables.ipynb\n",
    "print()\n",
    "print(\"Fraction who survived by sex and Pclass\")\n",
    "print(training_set.pivot_table('Survived', index='Sex', columns='Pclass'))\n",
    "\n",
    "print()\n",
    "print(\"Total number who survived vs. did not\")\n",
    "print(training_set['Survived'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z9v94wpg7N6Jqpgwej8ruyrN+vXrMFd3BFRWp55lo3n9fLxqhBAIa//3668Sd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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0a3f1da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Try scatterplot of Age and Fare. There's no pattern though...\n",
    "pyplot.scatter(training_set['Age'], training_set['Fare'], c=training_set['Survived'], \n",
    "               cmap='rainbow')\n",
    "pyplot.colorbar()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_features(data, training=True):\n",
    "    \"\"\"Take input DataFrame and return array of features to input into training models. \n",
    "    Modifies input in-place.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data : pandas.DataFrame\n",
    "        input data\n",
    "    training : boolean (optional)\n",
    "        flag for whether this is the training or test set (to decide whether to fit \n",
    "        Scaler) object, etc.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    X : numpy.array\n",
    "        2D array of cleaned up features for input into model\n",
    "    \"\"\"\n",
    "    # Encode categorical features\n",
    "    if training:\n",
    "        prepare_features.sex_enc = LabelEncoder() # Attribute of this function\n",
    "        prepare_features.sex_enc.fit(data['Sex'])\n",
    "    data['Sex'] = prepare_features.sex_enc.transform(data['Sex'])\n",
    "    \n",
    "    # get_dummies does one hot encoding and automatically handles nan's by making them all 0\n",
    "    data = data.join(pd.get_dummies(data['Embarked']))\n",
    "\n",
    "    # Impute missing values in Age and Fare using mean\n",
    "    data['Age'] = data['Age'].fillna(value=data['Age'].mean()) \n",
    "    data['Fare'] = data['Fare'].fillna(value=data['Fare'].mean()) \n",
    "\n",
    "    # Add name length as a feature to replace name (idea from a public kernel on kaggle)\n",
    "    data['Name Length'] = data['Name'].apply(len)\n",
    "\n",
    "    # Choose features to consider. Last few are from embarked\n",
    "    features = ['Age', 'Sex', 'Fare', 'Pclass', 'SibSp', 'Name Length', 'C', 'Q', 'S']\n",
    "\n",
    "    X = np.array(data[features]) # Features\n",
    "\n",
    "    # Scale features\n",
    "    if training:\n",
    "        prepare_features.scaler = StandardScaler()\n",
    "        prepare_features.scaler.fit(X)\n",
    "    X = prepare_features.scaler.transform(X)\n",
    "    \n",
    "    return X\n",
    "\n",
    "X = prepare_features(training_set, training=True)\n",
    "y = np.array(training_set['Survived']) # Outcomes\n",
    "    \n",
    "# Split to get a cross-validation set to better estimate performance\n",
    "X_train, X_cv, y_train, y_cv = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = SVC()\n",
    "\n",
    "# Train the model\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of test:  0.820627802691\n",
      "accuracy of training:  0.842814371257\n"
     ]
    }
   ],
   "source": [
    "predicted_y = clf.predict(X_cv)\n",
    "print(\"accuracy of test: \", accuracy_score(y_cv, predicted_y))\n",
    "predicted_y = clf.predict(X_train)\n",
    "print(\"accuracy of training: \", accuracy_score(y_train, predicted_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.832796150972\n"
     ]
    }
   ],
   "source": [
    "# Better method for cross-validation: multiple splits\n",
    "# https://jakevdp.github.io/PythonDataScienceHandbook/05.03-hyperparameters-and-model-validation.html\n",
    "scores = cross_val_score(clf, X, y, cv=4)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "0.832796150972\n"
     ]
    }
   ],
   "source": [
    "param_grid = [{'kernel':('linear', 'rbf'), 'C':[0.1, 1, 10, 100], 'gamma': [0.0001, 0.001, 0.01]}]\n",
    "\n",
    "svc = SVC()\n",
    "clf = GridSearchCV(svc, param_grid)\n",
    "clf.fit(X, y)\n",
    "# Check what the best parameters were\n",
    "print(clf.best_params_)\n",
    "model = clf.best_estimator_\n",
    "model = model.fit(X, y)\n",
    "scores = cross_val_score(model, X, y, cv=4)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.854430379747\n"
     ]
    }
   ],
   "source": [
    "test_set = pd.read_csv(\"test.csv\")\n",
    "\n",
    "X_test = prepare_features(test_set, training=False)\n",
    "\n",
    "y_test = model.predict(X_test)\n",
    "predictions = pd.DataFrame(data={'Survived': y_test}, index=test_set['PassengerId'])\n",
    "print (y_test.sum() / test_set['Survived'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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