Day_029_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業 : (Kaggle)鐵達尼生存預測\n",
    "***\n",
    "https://www.kaggle.com/c/titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>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>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>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>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>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",
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      ],
      "text/plain": [
       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2       3                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4       3                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "   SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1      1      0          PC 17599  71.2833   C85        C  \n",
       "2      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      1      0            113803  53.1000  C123        S  \n",
       "4      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 做完特徵工程前的所有準備 (與前範例相同)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import copy\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "data_path = '../data2/'\n",
    "df = pd.read_csv(data_path + 'titanic_train.csv')\n",
    "\n",
    "train_Y = df['Survived']\n",
    "df = df.drop(['PassengerId', 'Survived'] , axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
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       "      <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>1.0</td>\n",
       "      <td>0.121348</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.283951</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.769118</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.213483</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.481481</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.876471</td>\n",
       "      <td>0.139136</td>\n",
       "      <td>0.557823</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.396629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.983824</td>\n",
       "      <td>0.015469</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.305618</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.072059</td>\n",
       "      <td>0.103644</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016854</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.694118</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
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      "text/plain": [
       "   Pclass      Name  Sex       Age  SibSp  Parch    Ticket      Fare  \\\n",
       "0     1.0  0.121348  1.0  0.283951  0.125    0.0  0.769118  0.014151   \n",
       "1     0.0  0.213483  0.0  0.481481  0.125    0.0  0.876471  0.139136   \n",
       "2     1.0  0.396629  0.0  0.333333  0.000    0.0  0.983824  0.015469   \n",
       "3     0.0  0.305618  0.0  0.444444  0.125    0.0  0.072059  0.103644   \n",
       "4     1.0  0.016854  1.0  0.444444  0.000    0.0  0.694118  0.015713   \n",
       "\n",
       "      Cabin  Embarked  \n",
       "0  0.000000  1.000000  \n",
       "1  0.557823  0.333333  \n",
       "2  0.000000  1.000000  \n",
       "3  0.380952  1.000000  \n",
       "4  0.000000  1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 因為需要把類別型與數值型特徵都加入, 故使用最簡版的特徵工程\n",
    "LEncoder = LabelEncoder()\n",
    "MMEncoder = MinMaxScaler()\n",
    "for c in df.columns:\n",
    "    df[c] = df[c].fillna(-1)\n",
    "    if df[c].dtype == 'object':\n",
    "        df[c] = LEncoder.fit_transform(list(df[c].values))\n",
    "    df[c] = MMEncoder.fit_transform(df[c].values.reshape(-1, 1))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex         0.444180\n",
       "Pclass      0.111766\n",
       "Ticket      0.095739\n",
       "Fare        0.087733\n",
       "Name        0.082955\n",
       "Age         0.074354\n",
       "Cabin       0.071775\n",
       "Embarked    0.012220\n",
       "Parch       0.011514\n",
       "SibSp       0.007762\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 梯度提升樹擬合後, 將結果依照重要性由高到低排序 (note : D27作業中'Ticket'是第一名特徵, 'Age'是數值特徵中排名最高者)\n",
    "estimator = GradientBoostingClassifier()\n",
    "estimator.fit(df.values, train_Y)\n",
    "feats = pd.Series(data=estimator.feature_importances_, index=df.columns)\n",
    "feats = feats.sort_values(ascending=False)\n",
    "feats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 先用梯度提升機對鐵達尼生存預測做訓練，再用其特徵重要性回答下列問題\n",
    "\n",
    "# 作業1\n",
    "* 將特徵重要性較低的一半特徵刪除後，再做生存率預估，正確率是否有變化?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8215972695810002"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 原始特徵 + 梯度提升樹\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.811465936966161"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 高重要性特徵 + 梯度提升樹 \n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "high_feature = list(feats[:5].index)\n",
    "train_X = MMEncoder.fit_transform(df[high_feature])\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業2\n",
    "* 將特徵重要性最高的兩個特徵做特徵組合，是否能再進一步提升預測力?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "# 觀察重要特徵與目標的分布\n",
    "# 第一名 : Ticket              \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.regplot(x=df['Ticket'], y=train_Y, fit_reg=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "# 第二名 : Name        \n",
    "sns.regplot(x=df['Name'], y=train_Y, fit_reg=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8215972695810002"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 製作新特徵看效果\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}